ai integration engineer

The Hartford

<div><h2 class="jobSectionHeader"><b>Job Details</b></h2>
<div><div><div>Location:</div>
<div>Hartford, CT</div>
</div><div><div>Category:</div><div>
Data &amp; Analytics</div>
</div><div><div>Employment Type:</div>
<div>Full time
</div></div><div><div>Job Ref:</div><div>
R2626256-168</div>
</div></div><p></p><div><div>Sr Data Engineer - GE07BE<p>
We&rsquo;re determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals &ndash; and to help others accomplish theirs, too. Join our team as we help shape the future.<br>
</p><p></p><div><p>The Hartford is seeking a Senior AI Machine Learning Engineer within Employee Benefits Applied AI and Analytics (EB AIA) to help build, deploy, and sustain enterprise-scale predictive and applied AI solutions across pricing, underwriting, sales related EB business workflows. As a Senior AI/ML engineer you will manage and modernize the existing predictive model portfolio while helping the team expand into generative AI, agentic AI and other applied AI capabilities.</p></div><div><p>
The role is intended for a hands-on technical lead who can execute approved solution designs, deploy production-ready AI and ML components, operate reliable model pipelines, and guide junior engineers. The person should be able to translate architecture and design direction into working, governed, and production assets with minimal supervision.</p></div><div><p><b>
Team Description</b></p></div><div><p>
The Employee Benefits Applied AI and Analytics team provides insight, automation, and augmentation across the policy lifecycle for Employee Benefits customers and internal business stakeholders. EB AIA supports a portfolio that spans sales, pricing, underwriting, policy installation, renewal, service, and operational workflows.</p></div><div><p>
In addition to the existing portfolio of Predictive AI assets, the team is scaling an end-to-end AI-driven reimagination of EB underwriting and service organizations. The team partners closely with enterprise platform enablement team to apply consistent architecture and engineering practices while tailoring solutions for accuracy, transparency, scalability, and business usability.</p></div><div><p><b>
Primary Responsibilities</b></p></div><div><ul><li>Lead day-to-day engineering execution for the EB predictive model portfolio, including pricing and underwriting models, scoring pipelines, model refreshes, monitoring, data validations, and production support.</li></ul></div><div><ul><li>Build, deploy, and maintain AI/ML components and data pipelines that support applied AI use cases across pricing, underwriting, sales, service, renewal, and policy lifecycle workflows.</li></ul></div><div><ul><li>Implement approved solution designs from senior Applied AI Engineers, Architects, and Data Scientists; translate design patterns into tested, reliable production code and workflows.</li></ul></div><div><ul><li>Support the initial build-out of generative AI and agentic AI solutions, including prompt orchestration, retrieval-augmented generation patterns, evaluation workflows, guardrails, and integration with existing EB data and application ecosystems.</li></ul></div><div><ul><li>Develop and operate batch and near-real-time data/AI pipelines for model training, feature generation, inference, post-processing, business rules integration, and downstream consumption.</li></ul></div><div><ul><li>Deploy and sustain production AI services, jobs, APIs, and workflows in AWS and GCP environments using approved CI/CD, testing, observability, security, and operational practices.</li></ul></div><div><ul><li>Own implementation quality for assigned components, including code reviews, unit/integration testing, documentation, runbooks, production readiness checks, and incident response support.</li></ul></div><div><ul><li>Guide and mentor junior engineers by breaking down technical work, reviewing code, explaining model/data pipeline patterns, and ensuring consistent engineering practices.</li></ul></div><div><ul><li>Partner with Data Scientists, Data Engineers, Asset Owners, Underwriting, Pricing stakeholders to understand requirements, validate outputs, resolve data issues, and ensure model solutions fit business workflows.</li></ul></div><div><ul><li>Maintain model and pipeline governance artifacts, including lineage, model inputs/outputs, monitoring metrics, validation evidence, operational controls, and handoff documentation.</li></ul></div><div><ul><li>Identify risks, bottlenecks, and operational gaps in deployed AI/ML solutions and recommend practical improvements under the guidance of senior technical leadership.</li></ul></div><div><p><b>
Minimum</b><b> </b><b>Requirements</b></p></div><div><ul><li> Bachelor&rsquo;s degree in related field or 6+ years of equivalent experience in software engineering, data engineering, ML/DevOps engineering, applied AI engineering, or closely related technical roles.</li></ul></div><div><ul><li>Master's degree in computer science, engineering, information technology, MIS, data science, or related discipline preferred.</li></ul></div><div><ul><li>Strong hands-on expertise in Python, SQL, SDLC practices, Git-based development, automated testing, and production-grade code delivery.</li></ul></div><div><ul><li>Experience deploying and operating data, AI, or ML workloads in AWS and GCP, including cloud storage, managed compute, orchestration, IAM-aware access patterns, logging, and monitoring.</li></ul></div><div><ul><li>Experience with ML engineering concepts such as feature pipelines, model training workflows, batch scoring, inference services, model monitoring, drift detection, validation, retraining, and production support.</li></ul></div><div><ul><li>Ability to work within defined architecture, enterprise security standards, data governance expectations, coding standards, and operational controls.</li></ul></div><div><ul><li>Ability to lead implementation work, guide junior engineers, communicate tradeoffs, and manage multiple model/pipeline deliverables with limited day-to-day direction.</li></ul></div><div><p><b>
Preferred Experience</b></p></div><div><ul><li>Experience in insurance, employee benefits, pricing, underwriting, risk selection, sales enablement, or policy lifecycle analytics.</li></ul></div><div><ul><li>Experience supporting predictive model portfolios that require periodic refreshes, performance tracking, business validation, and governed production deployment.</li></ul></div><div><ul><li>Experience with generative AI or agentic AI implementation patterns, including RAG, prompt evaluation, LLM application integration, AI safety controls, human-in-the-loop workflows, and model output validation.</li></ul></div><div><ul><li>Experience with orchestration and workflow tools such as Airflow, Cloud Composer, Step Functions, Vertex AI Pipelines, or comparable enterprise platforms.</li></ul></div><div><ul><li>Experience with CI/CD, containers, APIs, infrastructure-as-code concepts, observability, and production incident management.</li></ul></div><div><p><b>
Success Profile</b></p></div><div><p>
A successful candidate will be a hands-on engineering lead who can take a generated or approved architecture, convert it into deployable assets, keep predictive AI models running reliably, and help the team move into applied AI delivery. The candidate should be comfortable doing implementation work across pricing and underwriting under senior supervision, while also raising the capability of junior engineers through practical technical guidance.</p><p><i>
This role will have a Hybrid work schedule, with the expectation of working in an office 3 days a week
</i></p><p><i>Candidates must be authorized to work in the US without company sponsorship. The company will not support the STEM OPT I-983 Training Plan endorsement for this position.</i></p></div><p><b>
Compensation</b></p><p>
The listed annualized base pay range is primarily based on analysis of similar positions in the external market. Actual base pay could vary and may be above or below the listed range based on factors including but not limited to performance, proficiency and demonstration of competencies required for the role. The base pay is just one component of The Hartford&rsquo;s total compensation package for employees. Other rewards may include short-term or annual bonuses, long-term incentives, and on-the-spot recognition. The annualized base pay range for this role is:</p>
$117,200 - $175,800<p>
Equal Opportunity Employer/Sex/Race/Color/Veterans/Disability/Sexual Orientation/Gender Identity or Expression/Religion/Age</p></div></div></div><p></p>

Senior AI Machine Learning Engineer

Occam Solutions

<div><p>Primary Responsibility</p><p></p><p><br>
The <b>DevSecOps Engineer</b> shall serve as the lead technical engineer responsible for designing, implementing, securing, automating, and sustaining the cloud infrastructure supporting the Office of Naval Research (ONR) Data &amp; Analytics environment. The DevSecOps Engineer shall lead the implementation and execution of the technical requirements identified in <b>PWS Section 4.5 &ndash; Technical Infrastructure and Platform Support</b>, including infrastructure automation, Continuous Integration/Continuous Deployment (CI/CD), cloud security, container orchestration, Infrastructure-as-Code (IaC), and operational sustainment of the FedRAMP High and DoD Impact Level 5 (IL5) cloud environment.</p><p></p><p><br>
The DevSecOps Engineer shall architect and maintain secure cloud infrastructure, automate deployment pipelines, support AI/ML platform integration, implement cloud-native security controls, monitor system health and security events, and ensure compliance with DoD cybersecurity requirements. This position will work closely with software developers, data engineers, cloud architects, cybersecurity personnel, and Government stakeholders to deliver resilient, scalable, and secure cloud services.</p><p></p><p><br>
Duties and Responsibilities</p>
<p>
The DevSecOps Engineer shall:</p>
<ul>
<li>Design, deploy, automate, and maintain secure cloud infrastructure supporting mission applications and analytics platforms.</li>
<li>Develop, implement, and manage automated CI/CD pipelines using tools such as Jenkins, GitLab CI/CD, or equivalent technologies.</li>
<li>Build and maintain Infrastructure-as-Code (IaC) solutions utilizing Terraform, Ansible, or similar automation frameworks.</li>
<li>Implement and manage containerized application environments using Docker, Kubernetes, or comparable orchestration platforms.</li>
<li>Configure, monitor, and optimize cloud-native security services including Web Application Firewalls (WAF), Intrusion Detection/Prevention Systems (IDS/IPS), endpoint protection, and Security Information and Event Management (SIEM) platforms.</li>
<li>Analyze security logs, audit events, vulnerability reports, and SIEM alerts to identify threats, recommend mitigation strategies, and support incident response activities.</li>
<li>Support continuous Authority to Operate (ATO) compliance through configuration management, security patching, vulnerability remediation, and security documentation.</li>
<li>Implement secure API management, identity and access management (IAM), secrets management, encryption, and least-privilege access controls.</li>
<li>Develop automated monitoring, logging, alerting, and operational dashboards to improve system reliability and performance.</li>
<li>Support deployment and operationalization of AI/ML platforms and services, including cloud-native machine learning environments.</li>
<li>Collaborate with software developers, data engineers, and cloud architects to integrate security throughout the software development lifecycle (SDLC).</li>
<li>Ensure cloud infrastructure complies with DoD cybersecurity policies, FedRAMP High requirements, DoD IL5 controls, and applicable security frameworks.</li>
<li>Support system scalability, resilience, disaster recovery, and operational continuity.</li>
<li>Document system architectures, deployment procedures, security configurations, and operational processes.</li></ul><br>
<p></p>
<p>
Minimum Qualifications</p>
<p>
The DevSecOps Engineer shall meet the requirements for the <b>&quot;DevSecOps Engineer &ndash; Intermediate&quot;</b> labor category as defined in the <b>CHESS ITES-3S</b> contract vehicle and possess the following additional qualifications:</p>
<ul>
<li>Seven (7) years of combined experience in DevSecOps, DevOps, SecOps, or MLOps engineering and development.</li>
<li>Five (5) years of hands-on experience designing, securing, implementing, and maintaining cloud environments, preferably within <b>AWS GovCloud</b>, <b>Azure Government</b>, or similarly regulated cloud environments.</li>
<li>Demonstrated experience supporting cloud environments operating under <b>FedRAMP High</b>, <b>DoD Impact Level 5 (IL5)</b>, or comparable federal security requirements.</li>
<li>Verifiable experience on at least two (2) projects designing, implementing, and managing Continuous Integration/Continuous Deployment (CI/CD) pipelines using Jenkins, GitLab CI/CD, Azure DevOps, GitHub Actions, or equivalent technologies.</li>
<li>Demonstrated expertise implementing and managing containerization and orchestration technologies, including Docker, Kubernetes, OpenShift, or similar platforms.</li>
<li>Hands-on experience implementing Infrastructure-as-Code (IaC) using Terraform, Ansible, CloudFormation, or comparable automation tools.</li>
<li>Experience configuring and administering cloud-native security capabilities including Web Application Firewalls (WAF), Intrusion Detection/Prevention Systems (IDS/IPS), Security Information and Event Management (SIEM), vulnerability management, and cloud security monitoring solutions.</li>
<li>Verifiable experience analyzing security logs, SIEM data, audit records, and threat intelligence to identify vulnerabilities, investigate security events, and implement corrective actions.</li>
<li>Experience implementing secure Identity and Access Management (IAM), encryption, secrets management, and Zero Trust security principles.</li>
<li>Experience supporting automated monitoring, logging, system performance optimization, and operational sustainment of enterprise cloud platforms.</li>
<li>Strong understanding of DevSecOps best practices, secure software development lifecycle (SSDLC), and cloud security architectures.</li><br>
</ul>
<p>Qualifications</p>
<ul>
<li>Active Secret security clearance (required).</li>
<li>Current DoD 8140 (or legacy DoD 8570) cybersecurity certification applicable to privileged access functions (e.g., Security+, CySA+, CASP+, CISSP, CCSP).</li>
<li>AWS Certified DevOps Engineer &ndash; Professional, AWS Security Specialty, Azure DevOps Engineer Expert, Certified Kubernetes Administrator (CKA), or comparable cloud certifications.</li>
<li>Experience supporting AI/ML environments, including AWS SageMaker, Azure Machine Learning, or similar platforms.</li>
<li>Experience with streaming data technologies such as Apache Kafka, AWS Kinesis, or equivalent event-driven architectures.</li>
<li>Familiarity with NIST RMF, DISA STIGs, FedRAMP, Zero Trust Architecture, and DoD cybersecurity compliance requirements.</li></ul></div>

DevSecOps Engineer

Dechert LLP

<p></p><div><div><div><div><div><div><div><div><div>Dechert, LLP is a global specialist law firm focused on high end legal work. The AI Engineer partners directly with attorneys, legal professionals, and business-services teams to identify, design, build, and deploy secure AI-enabled solutions that improve the delivery of legal services and firm operations. This role operates at the intersection of business discovery, rapid application development, artificial intelligence, automation, and enterprise technology.
</div><div></div><div>The role translates complex legal and operational needs into practical, scalable solutions&mdash;including generative AI applications, retrieval-augmented generation (RAG) solutions, workflow automations, intelligent agents, integrations, and custom web applications.
</div><div></div><div>The position is part of the IT Applications rapid-development function and is responsible for moving high-value opportunities from idea through proof of concept, pilot, production readiness, and transition to the appropriate enterprise applications, platform, or operations team for long-term support. The engineer works within the firm&rsquo;s security, privacy, data governance, architecture, and AI-risk-management standards.
 </div><div></div><div><b>ESSENTIAL JOB FUNCTIONS:</b>
</div><ul><li>Partner with attorneys, practice groups, legal project management, knowledge management, finance, risk, client development, and business-services teams to understand workflows, pain points, and desired outcomes.</li>
<li>Facilitate technical discovery sessions; assess business problems for AI, automation, workflow, integration, and custom application opportunities.</li>
<li>Rapidly design and develop proof-of-concept and pilot solutions using approved AI platforms, APIs, low-code tools, workflow automation platforms, and custom development technologies.</li>
<li>Build secure AI-enabled applications, including generative AI assistants, document and knowledge-search solutions, RAG applications, workflow copilots, intelligent agents, and decision-support tools.</li>
<li>Develop and maintain integrations among firm applications, document repositories, knowledge systems, data platforms, collaboration tools, and approved third-party services.</li>
<li>Design AI solutions that appropriately address data classification, confidentiality, ethical use, model limitations, human review, auditability, access controls, retention, and regulatory obligations.</li>
<li>Work with enterprise solutions architecture, information security, privacy, records management, risk management, and legal teams to ensure solutions meet global firm standards before production deployment.</li>
<li>Evaluate and select appropriate technical approaches based on business value, solution complexity, data sensitivity, scalability, supportability, and time-to-value.</li>
<li>Develop reusable components, integration patterns, prompt libraries, evaluation methods, documentation, and technical standards that accelerate future AI and automation delivery.</li>
<li>Conduct testing, quality assurance, user acceptance testing, model evaluation, and performance monitoring for AI-enabled solutions.</li>
<li>Establish solution success measures, such as time saved, adoption, accuracy, user satisfaction, process-cycle reduction, risk reduction, and business impact.</li>
<li>Create technical documentation, architecture diagrams, support materials, operating procedures, and knowledge-transfer artifacts.</li>
<li>Transition mature solutions to the appropriate applications support and operations or enterprise application development, ensuring clear ownership, support readiness, and lifecycle plans.</li>
<li>Monitor deployed solutions for performance, reliability, adoption, model behavior, security concerns, and opportunities for iterative improvement.</li>
<li>Stay current on AI engineering practices, legal-industry AI use cases, emerging tools, AI governance requirements, and applicable technology trends.</li>
<li>Participate in intake prioritization, solution estimation, roadmap planning, vendor evaluations, and innovation portfolio reporting.</li>
<li>Partner with Legal, Risk, Security, and Compliance Teams to ensure AI initiatives meet applicable legal and regulatory obligations.</li>
<li>Ensure AI systems handling client or confidential information meet applicable privacy, security, and data handling requirements across all operating jurisdictions.</li><br>
</ul><div></div><br>
<div><b>KNOWLEDGE:</b>
 </div><ul><li>Experience with Generative AI, Model Context Protocol (MCP), Azure/OpenAI, Large Language Model (LLM), Microsoft CoPilot, prompt engineering, retrieval-augmented generation, embeddings, vector databases, AI agents, model evaluation, and responsible AI practices.</li>
 <li>Application development concepts, including APIs, microservices, web applications, databases, authentication, authorization, logging, monitoring, testing, and CI/CD practices.</li>
 <li>Cloud and enterprise technology environments, including secure API integration, DevOps, identity and access management, data governance, and application lifecycle management.</li>
 <li>Automation and orchestration technologies, such as workflow platforms, robotic process automation, low-code/no-code development tools, and integration platforms.</li>
 <li>Data privacy, confidentiality, information security, records retention, and risk considerations applicable to professional-services or legal environments.</li>
 <li>Software development methodologies, including Agile, Kanban, rapid prototyping, product discovery, and iterative delivery.</li>
 <li>Legal-industry workflows and systems is preferred, including document management, knowledge management, time and billing, matter management, legal research, e-discovery, client intake, and financial systems.</li>
</ul><div><b>SKILLS:</b>
</div><ul><li>Strong software engineering skills in one or more modern programming languages, such as Python, JavaScript/TypeScript, C#, ASP.NET Core, SQL, or similar technologies.</li>
<li>Ability to build and deploy AI-enabled applications using APIs, SDKs, orchestration frameworks, cloud services, and enterprise platforms.</li>
<li>Ability to translate ambiguous business needs into clearly defined user stories, technical requirements, solution designs, and delivery plans.</li>
<li>Strong consultative and communication skills, with the ability to work effectively with attorneys, senior business leaders, technical teams, vendors, and nontechnical users.</li>
<li>Ability to explain AI capabilities, limitations, risks, and recommended controls in clear, practical language.</li>
<li>Strong analytical, problem-solving, and systems-thinking abilities.</li>
<li>Ability to balance speed and experimentation with security, quality, governance, maintainability, and long-term supportability.</li>
<li>Experience designing user-centered solutions and incorporating feedback into rapid iterations.</li>
<li>Strong organization and prioritization skills in a fast-moving environment with multiple concurrent initiatives.</li>
<li>Knowledge of responsible AI frameworks such as React and emerging international AI regulations.</li>
</ul><div><b>INTERESTS:</b>
</div><ul><li>Genuine curiosity about applying AI to meaningful legal, client-service, and business-operations challenges.</li>
<li>Interest in working directly with end users and seeing solutions through from discovery to measurable adoption.</li>
<li>Interest in responsible AI, data protection, human-centered design, and practical technology governance.</li>
<li>Enthusiasm for experimentation, continuous learning, emerging technologies, and building reusable enterprise capabilities.</li>
<li>Interest in improving how attorneys and professional-services teams access knowledge, complete work, collaborate, and serve clients.</li>
 </ul><div></div><div><b>EDUCATION AND EXPERIENCE:</b>
</div><div></div><ul><li>Bachelor&rsquo;s degree in Computer Science, Software Engineering, Information Systems, Data Science, Artificial Intelligence, or a related technical discipline required; equivalent combination of education, training, and relevant experience may be considered.</li>
<li>Minimum of 5 years of experience in software engineering, application development, automation, systems integration, data engineering, or related technical roles.</li>
<li>Minimum of 2 years of experience designing, developing, or deploying AI-enabled, machine-learning, generative AI, automation, or intelligent workflow solutions preferred.</li>
<li>Experience building applications using large language model APIs, RAG architectures, AI orchestration frameworks, Model Context Protocol (MCP), Azure/OpenAI, vector search technologies, or agentic workflow patterns strongly preferred.</li>
<li>Experience with cloud platforms such as Microsoft Azure or Amazon Web Services⁠.</li>
<li>Experience with enterprise integrations, APIs, identity and access management, secure development practices, and application lifecycle management required.</li>
<li>Experience in a law firm, legal technology provider, consulting firm, financial-services organization, or other regulated professional-services environment strongly preferred.</li>
<li>Experience working with cross-functional stakeholders and delivering technology solutions from discovery through production deployment required.</li>
<li>Relevant certifications in cloud engineering, AI, software development, security, automation, Agile delivery, or legal technology are preferred but not required.</li></ul></div></div></div></div></div></div></div></div><div></div>

AI Engineer

TAIT

<p></p><div>TAIT partners with artists, brands, IP holders and place makers to bring culture-defining, never-before-seen experiences to life. With a legacy of innovation spanning over 45 years, TAIT has grown from pioneering in rock &lsquo;n&rsquo; roll concert staging to setting the global standard for extraordinary live events and experiences through cutting-edge technology, precision engineering, and creative design. TAIT&rsquo;s 20 global offices have developed iconic productions and experiences in over 30 countries, all seven continents, and even outer space for renowned performers, theme parks, exhibits, and venues across the globe, including partnerships with Taylor Swift, Cirque Du Soleil, Royal Opera House, Nike, NASA, Bloomberg, Google, Beyonc&eacute;, and The Olympics<p><br>
TAIT is looking for a hands-on Data Engineer to build, scale, and operate the governed data platform that will underpin the next phase of the company's technology transformation. Reporting to the Data Architect, this role will turn the target data architecture into reliable pipelines, reusable data products, trusted analytical models, and production platform capabilities.</p><p>
This role is ideal for an experienced engineer who can move between architecture and execution and who has a proven track record delivering production-grade Databricks solutions in AWS. The successful candidate will do more than move data from one system to another: they will have delivered projects that combine complex enterprise and operational data to enable sophisticated analysis, forecasting, optimization, AI/ML use cases, and confident executive decision-making.</p><p>
The Data Engineer will work closely with the Data Architect and partner across Enterprise Technology, Architecture/AI, Integration, Infrastructure and Cloud, Information Security, and business teams including Finance, Sales, Project Delivery, Operations, Procurement, Manufacturing, and People/Resourcing. The role will help establish clear sources of truth and enable analytics across systems such as Salesforce CRM and CPQ, Kantata PSA, Epicor and the next-generation ERP, MES, Autodesk/PLM, HCM, service management platforms, and other internal and external data sources.</p><p></p><p><b><br>
Responsibilities:</b></p><p>
Databricks and AWS Data Platform Engineering</p><ul><li>
Design, build, deploy, and operate production data pipelines and lakehouse capabilities using Databricks on AWS.</li><li>
Implement scalable ingestion, transformation, orchestration, and serving patterns for batch, near-real-time, streaming, API, file, and change-data-capture workloads.</li><li>
Use AWS services and controls - including S3, IAM, KMS, VPC networking, Secrets Manager, CloudWatch, and appropriate catalog or integration services - to create secure and supportable data solutions.</li><li>
Develop reliable data layers, optimizing workloads, storage design, cluster or serverless configuration, performance, and platform cost.</li><li>
Establish automated deployment practices using Git, CI/CD, infrastructure as code, automated testing, and environment promotion across development, test, and production.</li><li>
Implement monitoring, observability, alerting, recovery, service-level objectives, and operational runbooks for critical data products and pipelines.</li></ul><p>
Analytics Enablement and Data Products</p><ul><li>
Translate high-value business questions into well-defined data products, curated datasets, and semantic-ready models for Power BI, advanced analytics, and AI-enabled solutions.</li><li>
Integrate data from multiple enterprise and operational sources to support strategic analytics, dashboards, KPIs, project and customer profitability, resource and capacity analysis, financial planning, forecasting, scenario modeling, operational optimization, and other sophisticated analytical use cases.</li><li>
Partner with analysts, product owners, and business subject-matter experts to ensure data products are analytically fit for purpose, clearly defined, and adopted by their intended users.</li><li>
Create reusable datasets and feature-ready data that accelerate experimentation and responsible delivery of machine learning, generative AI, and other advanced analytical capabilities.</li><li>
Define and track technical and business measures for data-product adoption, quality, timeliness, reliability, and realized value.</li></ul><p>
Roadmap Delivery</p><ul><li>
Partner with the Integration team and Data Architect to define data contracts, APIs, event patterns, and reusable integration standards, including appropriate alignment with MuleSoft and other enterprise integration capabilities.</li><li>
Support major platform implementations and migrations by developing conversion pipelines, reconciliation routines, archival strategies, historical-data models, and post-go-live analytical capabilities.</li><li>
Create repeatable onboarding patterns that accelerate M&amp;A data discovery, integration, harmonization, and reporting across acquired businesses.</li><li>
Reduce data and technology debt by replacing brittle point-to-point extracts, undocumented transformations, and duplicate datasets with governed, reusable platform services.</li></ul><p>
Data Quality, Governance, and Security</p><ul><li>
Work with the Data Architect to implement enterprise data standards, naming conventions, reference architectures, reusable patterns, and engineering guardrails.</li><li>
Build automated data-quality controls, source-to-target reconciliation, exception handling, freshness checks, and issue-management processes for critical data domains.</li><li>
Implement metadata management, cataloging, lineage, ownership, and access controls using Databricks governance capabilities and supporting enterprise tools.</li><li>
Apply security by design, including least-privilege access, encryption, secrets management, environment separation, auditability, retention, and appropriate handling of confidential or regulated data.</li><li>
Help define and maintain trusted sources of truth, master and reference-data practices, data definitions, and documentation that make strategic metrics traceable and explainable.</li></ul><p>
Delivery, Collaboration, and Continuous Improvement</p><ul><li>
Own engineering work from discovery through production support, including estimation, design, development, testing, documentation, deployment, adoption, and continuous improvement.</li><li>
Communicate technical tradeoffs, dependencies, risks, and recommendations clearly to both technical and non-technical audiences.</li><li>
Participate in design reviews, code reviews, incident reviews, architecture forums, and roadmap planning; provide practical feedback that improves quality without slowing delivery unnecessarily.</li><li>
Work effectively with global internal teams, nearshore and offshore partners, software vendors, and implementation partners while maintaining clear engineering accountability and standards.</li><li>
Mentor less-experienced engineers and analysts, share reusable practices, and help build a strong data-engineering discipline within GTS.</li></ul><p><b>
Required Qualifications:</b></p><ul><li>
Bachelor's degree in computer science, engineering, information systems, data science, or a related discipline, or equivalent practical experience.</li><li>
7+ years of professional experience in data engineering, analytics engineering, data platform engineering, or a closely related role.</li><li>
A proven track record designing, delivering, and operating production-grade solutions in Databricks on AWS, including responsibility for architecture implementation, pipeline development, deployment, support, performance, and cost.</li><li>
Evidence of delivering multiple end-to-end projects where engineered data products enabled sophisticated analysis - such as cross-system KPI reporting, forecasting, optimization, profitability analysis, anomaly detection, predictive modeling, scenario analysis, or AI/ML - with clear business outcomes.</li><li>
Advanced SQL and strong Python/PySpark skills, including experience developing maintainable, tested, production-quality code for large and complex datasets.</li><li>
Strong knowledge of Spark, Delta Lake or comparable lakehouse patterns, dimensional and analytical data modeling, partitioning, schema evolution, data contracts, and performance tuning.</li><li>
Hands-on experience with core AWS data-platform capabilities such as S3, IAM, KMS, VPC networking, CloudWatch, Secrets Manager, and related services used to secure, integrate, and operate Databricks workloads.</li><li>
Experience building batch and incremental pipelines using APIs, files, databases, SaaS connectors, change data capture, and at least one orchestration framework or managed workflow capability.</li><li>
Experience with Git-based development, CI/CD, automated data testing, code review, environment management, and infrastructure-as-code practices.</li><li>
Practical experience implementing data quality, observability, cataloging, lineage, access control, and operational support for business-critical data products.</li><li>
Strong problem-solving, documentation, and communication skills, including the ability to translate ambiguous business needs into pragmatic technical solutions and explain data issues in business terms.</li><li>
Ability to work independently in a changing environment, prioritize based on business value and risk, and collaborate effectively across architecture, integration, infrastructure, security, applications, analytics, and business teams.</li></ul><p><b>
Preferred Qualifications:</b></p><ul><li>
Databricks certification and/or AWS certification relevant to data engineering, analytics, or cloud architecture.</li><li>
Experience with Unity Catalog or comparable governance capabilities, including fine-grained access, lineage, cataloging, and multi-environment administration.</li><li>
Experience preparing governed data models for Power BI, including semantic-model design, incremental refresh patterns, performance optimization, and row-level security.</li><li>
Experience integrating enterprise applications such as Salesforce, Epicor or another ERP, Kantata or another PSA, CPQ, MES, PLM, HCM, EPM, and service management platforms.</li><li>
Experience with MuleSoft or another enterprise integration platform and with designing API- or event-driven data exchange patterns.</li><li>
Experience enabling machine learning or AI solutions through feature engineering, model-ready datasets, ML lifecycle tooling, vector or unstructured-data pipelines, or responsible AI data controls.</li><li>
Experience in a global, project-based, manufacturing, engineering, live-entertainment, or operationally complex business environment.</li><li>
Experience supporting enterprise transformation, ERP replacement, legacy-platform retirement, cloud migration, and/or M&amp;A integration.</li></ul><p></p><p><br>
#LI-ML1</p>
TAIT is an equal opportunity employer fully committed to diversity and inclusion in the workplace. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran or any other protected characteristic as outlined by international, national, state, or local laws.</div>

Data Engineer

Google

<div><h3 class="jobSectionHeader"><b>Minimum qualifications:</b></h3><ul>
<li>Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, or a related field, or equivalent practical experience.</li>
<li>8 years of experience in ASIC design or RTL design.</li>
<li>Experience with SoC design and integration.</li>
<li>Experience with multi-chiplet SoC integration.</li>
<li>Experience with SoC Design for Testing (DFT) planning.</li>
<li>Experience scripting in Python or Perl or Tcl.</li></ul><br>
<h3 class="jobSectionHeader"><b>
Preferred qualifications:</b></h3><ul>
<li>Master's degree or PhD in Electrical Engineering, Computer Engineering or Computer Science, with an emphasis on computer architecture.</li>
<li>Experience with SoC signoff&ndash;Logic Equivalence Check (LEC)/Lint/Unified Power Format (UPF)/CDC/Reset Domain Crossing (RDC) flows.</li>
<li>Cross-functional experience with Design Verification (DV), Physical Design (PD), Clock Domain Crossing (CDC) and DFT teams.</li>
<li>Experience designing and constructing ASIC Input/Output (IO) pad-rings.</li>
<li>Knowledgeable in 3DIO methodology, voltage and clock domain partitioning.</li>
<li>Knowledgeable in ASIC top-level Static Timing Analysis (STA) and clock planning.</li>
</ul><div><h2 class="jobSectionHeader"><b>About the job</b></h2><p>
In this role, you&rsquo;ll work to shape the future of AI/ML hardware acceleration. You will have an opportunity to drive TPU (Tensor Processing Unit) technology that powers Google's most demanding AI/ML applications. You&rsquo;ll be part of a team that pushes boundaries, developing custom silicon solutions that power the future of Google's TPU. You'll contribute to the innovation behind products loved by millions worldwide, and leverage your design and verification expertise to verify complex digital designs, with a specific focus on TPU architecture and its integration within AI/ML-driven systems.</p>
<p>
As an ASIC SoC Integration Engineer, you will drive the construction and architectural execution of advanced multi-chiplet SoC integration projects. This role demands deep cross-domain collaboration, working closely with ASIC Packaging and Interposer teams to orchestrate complex interposer planning and implementation, alongside multi-die DFT connectivity and packaging routing. You will leverage your understanding of multi-die integration concerns, 3DIO technologies, and voltage and clock domain partitioning to build robust systems while addressing critical physical design implementation concerns. Utilizing your experience in architecting RTL solutions with software-based construction, instantiation, and generation, you will manage SoC IP integration and participate in layout design decisions and SoC-level sign-off activities, shaping the future of high-performance silicon layout. You will own the upper levels of RTL hierarchy up-to the package level and work closely with the Verification and Emulation teams to debug connectivity issues and optimize RTL compilation. You will manage RTL deliverables to all stakeholders and plan RTL integration activities to meet project schedules.</p><p>
The AI and Infrastructure team is redefining what&rsquo;s possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide.
</p><p>We're the driving team behind Google's groundbreaking innovations, empowering the development of our AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world-leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more.</p>
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
<br>
<br>
US: $163000 - $236000 (USD) + 15% bonus target + equity + benefits<br>
<br>
Learn more about benefits at Google.</div><div><h2 class="jobSectionHeader"><b>
Responsibilities</b></h2><ul>
<li>Lead advanced SoC integration projects from concept to final delivery, managing project milestones, deliverables, and cross-functional schedules.</li>
<li>Partner with ASIC teams on complex interposer and package design, and resolve multi-die physical design challenges leveraging 3DIO and domain partitioning expertise.</li>
<li>Utilize software-based construction to seamlessly integrate SoC IPs and maintain complete ownership of the upper-level RTL hierarchy up to the package level.</li>
<li>Collaborate closely with Verification and Emulation teams to resolve complex connectivity issues and optimize system compilation.</li>
<li>Participate in critical layout design decisions and SoC-level sign-off activities to ensure robust, high-performance silicon delivery.</li>
</ul></div>Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form.</div>

TPU SoC Integration Engineer, Cloud

Coforge

AI Engineer -Practitioners who build and deploy AI models and agentic workflows in production, including prompt engineering, fine-tuning, RAG pipelines, and AI-infused application development. These individuals design, develop, customize, and implement AI solutions for others to use. Technology - Java, Frontend and QE (Playwright).
<br>We are seeking an experiencedAI Engineer to design, develop, and deploy AI-powered applications and agentic workflows into production environments. The ideal candidate will have hands-on expertise in building AI solutions using Large Language Models (LLMs), Retrieval Augmented Generation (RAG), prompt engineering, AI orchestration frameworks, and modern application development practices.
<br>This role requires a combination ofAI engineering, Java development, Frontend development, and Quality Engineering (Playwright) to deliver scalable, reliable, and enterprise-grade AI solutions.
<br><br>
Key Responsibilities
<br><br>
AI Solution Development
<br><br>
<ul>
<li>Design, develop, and deploy AI-powered applications using modern LLM technologies.</li>
<li>Build and implement agentic workflows leveraging AI agents, orchestration frameworks, and automation platforms.</li>
<li>Develop Retrieval-Augmented Generation (RAG) pipelines using vector databases and enterprise knowledge sources.</li>
<li>Perform prompt engineering, prompt optimization, and model evaluation to improve AI solution accuracy and reliability.</li>
<li>Fine-tune foundation models where applicable to meet business-specific requirements.</li>
<li>Integrate AI capabilities into existing enterprise applications and platforms.</li>
<li>Monitor, optimize, and maintain AI systems in production environments.</li>
</ul>
Application Development
<br><br>
<ul>
<li>Develop backend services and APIs using Java-based frameworks.</li>
<li>Build responsive and scalable frontend applications to deliver AI-driven user experiences.</li>
<li>Collaborate with architects, product owners, and business stakeholders to translate requirements into technical solutions.</li>
<li>Implement integrations with internal and external systems through APIs and microservices.</li>
</ul>
Quality Engineering Testing
<br><br>
<ul>
<li>Develop automated test strategies for AI-enabled applications.</li>
<li>Design and execute UI and API automation using Playwright.</li>
<li>Validate AI outputs through functional, regression, and performance testing.</li>
<li>Establish test frameworks and quality gates for AI solutions deployed in production.</li>
<li>Ensure security, performance, and reliability standards are met.</li>
</ul>
Collaboration Innovation
<br><br>
<ul>
<li>Work closely with AI Architects, Data Scientists, Product Teams, and Business SMEs.</li>
<li>Stay current with emerging AI technologies, frameworks, and industry best practices.</li>
<li>Contribute to AI governance, responsible AI practices, and solution documentation.</li>
</ul>
Required Skills
<br><br>
AI GenAI
<br><br>
<ul>
<li>Prompt Engineering</li>
<li>Large Language Models (OpenAI, Azure OpenAI, Anthropic, Gemini, etc.)</li>
<li>Agentic AI Frameworks</li>
<li>Retrieval-Augmented Generation (RAG)</li>
<li>Vector Databases</li>
<li>Model Evaluation and Optimization</li>
<li>Fine-tuning Concepts</li>
<li>AI Application Development</li>
<li>AI Orchestration Frameworks (LangChain, LangGraph, Semantic Kernel, CrewAI, AutoGen, etc.)</li>
</ul>
Backend Development
<br><br>
<ul>
<li>Java 8+/11+/17+</li>
<li>Spring Boot</li>
<li>REST APIs Microservices Architecture</li>
<li>API Integration</li>
</ul>
Frontend Development
<br><br>
<ul>
<li>React.js / Angular / Modern JavaScript Frameworks</li>
<li>HTML5, CSS3, JavaScript, TypeScript</li>
</ul>
Quality Engineering
<br><br>
<ul>
<li>Playwright Automation</li>
<li>API Testing</li>
<li>Test Automation Framework Design</li>
<li>Functional Regression Testing</li>
<li>CI/CD Testing Practices</li>
</ul>
Cloud DevOps
<br><br>
<ul>
<li>Azure / AWS / GCP</li>
<li>Docker Kubernetes</li>
<li>CI/CD Pipelines</li>
<li>Git, GitHub, Azure DevOps</li>
</ul>


Syracuse University

<div><div><div><div><b>Job #</b></div> <div>042947</div>
</div><br>
<div><div><b>Department Code</b></div> <div>20701-9701</div>
</div><br>
<div><div><b>Department</b></div> <div>Information Technology Services</div>
</div><br>
<div><div><b>Job Title</b></div> <div>AI Data Engineer</div>
</div><br>
<div><div><b>Location</b></div> <div>Syracuse, NY</div>
</div><br>
<div><div><b>Campus</b></div> <div>Syracuse, NY</div>
</div><br>
<div><div><b>Commitment to On-Campus Experience</b></div>
<div>Syracuse University is committed to delivering an exceptional student experience through vibrant, engaged campus communities. This position is based at the above campus location and requires regular in-person presence to support our students, collaborate with colleagues, and contribute to our thriving academic environment. Syracuse University values the collaboration, mentorship, and spontaneous connections that happen when our community works together on campus. Remote work arrangements are limited in accordance with University policy.</div>
</div><br>
<div><div><b>Pay Range</b></div> <div>$89,000 - $95,000</div>
</div><br>
<div><div><b>Pay Determination</b></div>
<div>Pay rates at Syracuse University are based on a combination of factors including, but not limited to, the job responsibilities; the candidate&rsquo;s education, training, work experience and key competencies; the university&rsquo;s strategic priorities; internal peer equity; applicable federal, state, local laws, grant funding and contractual requisites; and external market analyses.</div>
</div><br>
<div><div><b>Staff Level</b></div> <div>S5</div>
</div><br>
<div><div><b>FLSA Status</b></div> <div>Exempt</div>
</div><br>
<div><div><b>Hours</b></div>
<div><div>Standard University business hours<br>
<br>
8:30am &ndash; 5:00pm (academic year)<br>
8:00am &ndash; 4:30pm (summer)</div><div>
<br>
Hours may vary based on operational needs.</div></div>
</div><br>
<div><div><b>Job Type</b></div> <div>Full-time</div>
</div><br>
<div><div><b>Unionized Position Code</b></div> <div>Not Applicable</div>
</div><br>
<div><div><b>Job Description</b></div>
<div>The AI Data Engineer leverages AI-assisted tools (e.g., code generation, chat-style assistants, agentic workflows) to accelerate pipeline development, documentation, and problem-solving that transforms structured and unstructured data into enterprise-ready assets to power AI and analytics solutions. Built on Microsoft Fabric, Syracuse University&rsquo;s standard data and analytics platform, this role bridges raw data sources with generative AI applications across OneLake, ensuring data quality, compliance, and scalability. Joining an established Enterprise Data &amp; AI team already building in Fabric, the AI Data Engineer serves as a key technical contributor to large-scale, university-wide initiatives, both current and emerging, that advance institutional strategy. At times, the incumbent may be dedicated to specific, university initiatives or partner units based on institutional priorities and cross-departmental projects.</div>
</div><br>
<div><div><b>Education and Experience</b></div>
<ul><li>Bachelor&rsquo;s degree in Artificial Intelligence, Computer Science, Data Science, or related field, or equivalent combination of education and experience.</li>
<li>4+ years of experience in data engineering, AI/ML integration, or enterprise IT.</li>
<li>Experience in a higher education IT environment preferred.</li>
</ul>
</div><br>
<div><div><b>Skills and Knowledge</b></div>
<ul><li>Expertise in data wrangling for structured and unstructured data.</li>
<li>Proficiency in SQL and at least one programming language (Python, Java, or C#).</li>
<li>Experience with Microsoft Fabric (OneLake, Data Factory, Real-Time Intelligence, Fabric IQ, notebooks) and comparable data platforms</li>
<li>Proficiency with Power BI and semantic modeling (data modeling, relationships, DAX, and OneLake-integrated semantic models) to enable trusted self-service analytics.</li>
<li>Familiarity with API development, microservices, and Model Context Protocol (MCP) integrations.</li>
<li>Experience with cloud infrastructure (Azure, AWS, GCP), containerization (Docker, Kubernetes), and serverless platforms (e.g., Logic Apps).</li>
<li>Familiarity in deploying AI/ML platforms (Azure AI Foundry, Google Vertex, Amazon Bedrock, OpenAI, etc.).</li>
<li>Demonstrated familiarity using generative AI tools to improve personal and team productivity.</li>
<li>Understanding of data governance, privacy, and ethical AI principles.</li>
<li>Strong problem-solving, analytical, and collaboration skills.</li>
<li>Excellent written and verbal communication skills, including the ability to translate complex technical concepts for non-technical audiences and to deliver presentations, demonstrations, and briefings to campus stakeholders and leadership.</li>
</ul>
</div><br>
<div><div><b>Responsibilities</b></div>
<div><b>Data Engineering &amp; Pipeline Development</b><br>
Design and implement scalable pipelines that ingest, clean, transform, and aggregate data from diverse sources (ERP, LMS, research systems, APIs, and external datasets) into formats optimized for AI and analytics. Ensure data quality, integrity, and reproducibility through robust engineering practices.<br>
<br>
<b>Integration &amp; Platform Support
</b><br>
Build connectors, workflows, and APIs to unify and operationalize data across cloud and on-premises platforms. Support deployment and lifecycle management of AI/ML models, ensuring seamless integration with enterprise applications.<br>
<br>
<b>Governance, Security &amp; Compliance
</b><br>
Maintain metadata, lineage, and documentation to support transparency and auditability. Ensure adherence to Syracuse University&rsquo;s ISF, FERPA, HIPAA, and other regulatory requirements, while applying ethical AI and data governance principles.<br>
<br>
<b>Collaboration &amp; Stakeholder Engagement
</b><br>
Gather and translate requirements from academic and administrative units to deliver tailored solutions aligned with institutional priorities. Represent AI/ML teams in cross-campus meetings, working groups, and governance bodies. Deliver presentations and demonstrations to technical and non-technical audiences.<br>
<br>
<b>Innovation &amp; Mentorship
</b><br>
Provide technical mentorship on data engineering and integration best practices. Anticipate and scale for future institutional needs (e.g., MCP, serverless, containerization, and generative AI) to drive innovation in data and AI adoption.</div>
</div><br>
<div><div><b>Physical Requirements</b></div>
<div>Not Applicable</div>
</div><br>
<div><div><b>Tools/Equipment</b></div>
<div>Not Applicable</div>
</div><br>
<div><div><b>Application Instructions</b></div>
<div>In addition to completing an online application, please attach a resume and cover letter.</div>
</div><br>
<div><div><b>About Syracuse University</b></div>
<div>Syracuse University is a private, international research university with distinctive academics, diversely unique offerings, and an undeniable spirit. Located in the geographic heart of New York State, with a global footprint, and over 150 years of history, Syracuse University offers a quintessential college experience.<br>
<br>
The scope of Syracuse University is a testament to its strengths: a pioneering history dating back to 1870; a choice of more than 200 majors, 100 minors, and 200 advanced degree programs offered across the University&rsquo;s 13 schools and colleges; over 15,000 undergraduates and over 6,000 graduate students; more than a quarter of a million alumni in 160 countries; and a student population from all 50 U.S. states and 123 countries. For more information, please visit http://www.syracuse.edu.</div>
</div><br>
<div><div><b>About the Syracuse area</b></div>
<div>Syracuse is a medium-sized city situated in the geographic center of New York State approximately 250 miles northwest of New York City. The metro-area population totals approximately 500,000. The area offers a low cost of living and provides many social, cultural, and recreational options, including parks, museums, festivals, professional regional theater, and premier shopping venues. Syracuse and Central New York present a wide range of seasonal recreation and attractions ranging from water skiing and snow skiing, hiking in the Adirondacks, touring the historic sites, visiting wineries along the Finger Lakes, and biking on trails along the Erie Canal.</div>
</div><br>
<div><div><b>EEO Statement</b></div>
<div>Syracuse University is an equal-opportunity institution. The University prohibits discrimination and harassment based on race, color, creed, religion, sex, gender, national origin, citizenship, ethnicity, marital status, age, disability, sexual orientation, gender identity and gender expression, veteran status, or any other status protected by applicable law to the extent prohibited by law. This nondiscrimination policy covers admissions, employment, and access to and treatment in University programs, services, and activities.</div>
</div><br>
<div><div><b>Commitment to Supporting and Hiring Veterans</b></div>
<div>Syracuse University has a long history of engaging veterans and the military-connected community through its educational programs, community outreach, and employment programs. After World War II, Syracuse University welcomed more than 10,000 returning veterans to our campus, and those veterans literally transformed Syracuse University into the national research institution it is today. The University&rsquo;s contemporary commitment to veterans builds on this historical legacy, and extends to both class-leading initiatives focused on making an SU degree accessible and affordable to the post-9/11 generation of veterans, and also programs designed to position Syracuse University as the employer of choice for military veterans, members of the Guard and Reserve, and military family members.</div>
</div><br>
<div><div><b>Commitment to a Respectful and Welcoming Community</b></div>
<div>Syracuse University fosters a welcoming learning environment where students, faculty, administrators, staff, curriculum, social activities, governance, and all aspects of campus life reflect a broad range of perspectives and experiences. The University community values the many similarities and differences among individuals and groups. At Syracuse, we are committed to preparing students to engage with and appreciate the richness of backgrounds, beliefs, and experiences that shape our society. To achieve this, we strive to cultivate a community that respects and encourages open dialogue, understanding, and mutual respect.</div></div></div></div><div></div>

AI Data Engineer

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