ai engineer

Zoolatech

<div><div><div><div><div><div><div><div>OVERVIEW</div>
</div><div><div>RESPONSIBILITIES</div>
</div><div><div>REQUIREMENTS</div>
</div></div></div></div><div><div><div><p>ATRC's Data &amp; AI Office is a product and platform execution team &mdash; not a traditional enterprise data function. We build and operate AI-enabled systems that create measurable operational impact across the organization. Our primary output is working software in production.</p><p><b>
What we are building &mdash; now:</b></p><ul><li><p>
An executive reporting portal serving senior leadership, integrating live data from SAP, Microsoft 365, SuccessFactors, and internal systems across multiple operational views (finance &amp; budget, KPIs, technology portfolio, news, wellbeing, and a personalized executive home)</p></li><li><p>
A sovereign data and AI platform: data warehouse, governed pipelines, metadata catalog, and classification enforcement across all entities</p></li><li><p>
Data mesh architecture with federated ownership &mdash; each entity owns its data products; the platform provides governed access</p></li><li><p>
Data readiness for the organization's agentic AI platform &mdash; clean, structured, governed data so AI agents can operate on trusted sources</p></li><li><p>
Operational AI agents delivering real value on top of the agentic platform (document triage, anomaly detection, automated briefings, classification automation)</p></li><li><p>
AI-enabled situational awareness and crisis management systems</p></li><li><p>
Data governance and classification &mdash; managing an active external vendor engagement, bridging framework deliverables into platform-enforced rules, and building the ongoing</p><p>
governance operations the organization requires</p></li></ul><p><b>
How we work:</b></p><ul><li><p>
Small team, high ownership &mdash; every engineer owns a module end-to-end</p></li><li><p>
Two-week sprints with demonstrated output at every review</p></li><li><p>
Ship and improve &mdash; working systems beat perfect designs</p></li><li><p>
Core team owns all architecture and product decisions; external capacity executes within defined scope</p></li><li><p>
Decisions take hours, not weeks. Daily technical choices are made by the engineer.</p></li><li><p>
Feature scope decisions take 24 hours. Architecture pivots take 48 hours. Nothing sits in limbo.</p></li><li><p>
Oversee 5 other workstreams powered by 3rd party vendors within the ecosystem but whom we are ultimately accountable for</p></li></ul>
</div><div><p>Builds the AI capabilities that operations depend on &mdash; classification automation, production agents, and the data bridge to the agentic platform.</p><p><b>
Mission</b></p><p>
Build, own, and continuously improve the AI capabilities across the office's production systems, working under the direction of the Principal AI Engineer. Your scope: automated data classification (remediating what's currently broken), AI-generated briefings, and operational AI agents that deliver real value in weeks.</p><p>
An external partner is building an agentic platform that ATRC will use. Your role in that relationship: define the data contracts the platform consumes, evaluate the sample agents delivered, and build the production agents on top of the platform that go beyond demonstrations into real operational use. You don't build the platform &mdash; you make sure ATRC's data is ready for it and that what gets built on top of it actually works.</p><p><b>
What This Role Owns</b></p><ul><li><p>
Data classification automation: implementing automated classification to remediate current failures, embedding classification into data pipelines alongside the Governance</p></li><li><p>
Lead Operational AI agents: building production agents on top of the agentic platform &mdash; going beyond the sample agents the external partner delivers into real operational workflows</p></li><li><p>
Agentic platform data contracts: defining what data the platform needs, in what format, with what quality guarantees &mdash; working with the Principal AI Engineer</p></li><li><p>
AI service implementation: FastAPI service around LLM APIs with versioned prompt templates</p></li><li><p>
Classification and briefing prompts: structured prompts returning validated JSON with tags, confidence levels, source attribution</p></li><li><p>
Prompt versioning: templates in configuration, editable without code changes</p></li><li><p>
Observability: every LLM call logged with input hash, model version, output, latency, token count</p></li><li><p>
Fallback logic: graceful degradation when LLM APIs are unavailable</p></li><li><p>
Quality evaluation: running precision/recall evaluations against human reviewer samples, reporting results, iterating prompts</p></li></ul><p><b>
Key Decisions</b></p><ul><li><p>
Prompt implementation &mdash; few-shot examples, output parsing, error handling</p></li><li><p>
Confidence threshold tuning based on evaluation results</p></li><li><p>
Context window packing &mdash; what data fits within token budgets</p></li><li><p>
Fallback behavior when APIs degrade</p></li><li><p>
Agent evaluation &mdash; whether an external partner's sample agent is production-ready or needs rework</p></li><li><p>
Bug triage for AI components &mdash; autonomous fix vs. escalate to Principal AI Engineer</p></li></ul><p><b>
Does Not Do</b></p><ul><li><p>
Define AI architecture or agent design patterns (Principal AI Engineer)</p></li><li><p>
Build the agentic platform itself (external partner)</p></li><li><p>
Build the backend API or data pipelines</p></li><li><p>
Fine-tune or train models</p></li><li><p>
Define governance policies (Governance Lead)</p></li></ul><p><b>
Ideal Candidate</b></p><p>
Has shipped an LLM-based feature that non-technical users depend on daily &mdash; and has been responsible when it breaks. Knows the hardest part of applied AI is the fallback, the observability, and the human review loop &mdash; not the prompt. Can evaluate someone else's agent demo and quickly identify whether it's production-ready or held together with string. Works well under a senior AI lead &mdash; takes architectural direction and executes with high quality and speed. Not attached to a particular model &mdash; the job is reliable output.</p>
</div><div><ul><li><p>LLM APIs (Claude, GPT-4, open-weight models) &mdash; structured output, JSON mode, system prompts</p></li><li><p>
Prompt engineering for classification &mdash; zero-shot and few-shot</p></li><li><p>
Python &mdash; async API calls, retry logic, exponential backoff</p></li><li><p>
LLM evaluation &mdash; precision/recall, human-AI agreement scoring</p></li><li><p>
Structured output &mdash; JSON schema enforcement, Pydantic validation</p></li><li><p>
Open-weight / sovereign model APIs (Falcon, Llama, or equivalent)</p></li><li><p>
Token budgeting and context window management</p></li><li><p>
AI observability &mdash; output quality monitoring, anomaly detection</p></li><li><p>
FastAPI and Docker</p></li></ul>
</div></div></div><div><div><div><div><p>Location:
</p><div>Istanbul, Turkey
</div></div><div><p>Seniority:
</p><div>Senior
</div></div><div><p>Technologies:
</p><div>Python
</div></div></div></div><div><div><p>Benefits:
</p><ul><li><b>Paid Vacation
</b></li><li><b>Hybrid Work (home/office)
</b></li><li><b>Sick Days
</b></li><li><b>Sport/Insurance Compensation
</b></li><li><b>Holidays Day Off
</b></li><li><b>English Classes
</b></li><li><b>Training Compensation
</b></li><li><b>Transportation compensation</b></li></ul></div></div></div></div></div></div>

AI Engineer

Blue Matter

<div><p>Blue Matter is a rapidly growing management consultancy focused on the biopharmaceutical industry. We partner with clients to help them achieve commercial success across the lifecycle of their products, portfolios, and organizations.</p><p>
Blue Matter is hiring for the <b>Technology and Operations Practice within the Blue Matter Insights Team</b>. Our Technology &amp; Operations practice integrates <b>strategy, data, and AI </b>to deliver scalable, technology-enabled solutions for BioPharma Clients. The practice has developed a <b>category-defining platform called Twine&trade;</b>&mdash; an AI-first customer intelligence platform purpose-built for life sciences that is already seeing strong traction in the market. The successful candidate will play a <b>direct role in shaping, enhancing, and scaling Twine </b>as a core capability within our portfolio.</p><p>
As an <b>AI Engineer / Senior AI Engineer</b>, you will work across Blue Matter as a technical expert in AI and emerging technologies, playing a key role in designing, building, and deploying AI-powered solutions for both internal teams and clients. You will be at the forefront of integrating APIs, building proof-of-concepts (POCs), developing internal applications, and creating client-facing tools and services. This role is ideal for someone who is passionate about staying current with the rapidly evolving AI landscape and eager to apply cutting-edge techniques to solve real-world problems in life sciences.</p><p><b>
Primary Responsibilities</b></p><p>
As a key member of cross-functional client-facing teams, develop and execute project plans to support initiatives that include, but are not limited to:</p><ul><li>
Design, develop, and deploy AI/ML solutions and Generative AI applications to support commercial data and analytics for life sciences companies</li><li>
Integrate third-party AI APIs (e.g., OpenAI, Anthropic, Google Gemini, Hugging Face) into internal and client-facing applications to deliver intelligent, scalable solutions</li><li>
Build proof-of-concepts (POCs) and rapid prototypes to demonstrate the value of AI-driven approaches for product launch analytics, drug discovery, patient analytics, and data operations</li><li>
Develop and maintain internal tools and applications that streamline workflows, automate operational processes, and enhance team productivity using AI</li><li>
Build client-facing tools and services that leverage AI to deliver actionable insights, interactive dashboards, and intelligent recommendations</li><li>
Utilize cloud-service providers (e.g., AWS, Azure, or GCP) including services such as S3, EC2, Lambda, Azure OpenAI, and SageMaker to build and deploy scalable AI solutions</li><li>
Collaborate with onshore/offshore teams to ensure timely, high-quality deliverables, providing technical guidance and code reviews</li><li>
Design and implement data pipelines and ETL processes to support AI model training, fine-tuning, and inference workflows</li><li>
Investigate and evaluate emerging AI frameworks, tools, and models to continuously improve the team&rsquo;s AI capabilities and stay ahead of industry developments</li><li>
Contribute to Blue Matter&rsquo;s Internal platform rollout and other AI-powered product initiatives in client environments</li><li>
Address client questions, gather requirements, and deliver AI-powered solutions aligned with business goals</li><li>
Ensure project success through effective collaboration and meeting deadlines using agile delivery model</li><li>
Stay current with the latest advancements in AI/ML, LLMs, prompt engineering, RAG, agents, and related technologies, and evangelize best practices across the organization</li></ul><p><b>
Desired Experience and Skills</b></p><ul><li>
Hands-on experience building AI/ML applications, including working with LLMs, Generative AI, and NLP frameworks</li><li>
Strong proficiency in Python and experience with AI/ML libraries and frameworks (e.g., LangChain, LlamaIndex, Hugging Face Transformers, PyTorch, TensorFlow)</li><li>
Proven experience integrating AI APIs (e.g., OpenAI, Anthropic, Google Vertex AI, Azure OpenAI) into production-grade applications</li><li>
Experience building and deploying internal tools and applications (e.g., using Streamlit, Gradio, Flask, FastAPI, or React)</li><li>
Ability to build client-facing tools and services that are reliable, user-friendly, and production-ready</li><li>
Expertise in building proof-of-concepts (POCs) and rapid prototypes to validate AI-driven solutions</li><li>
Experienced with cloud platforms (AWS, Azure, GCP) and services</li><li>
Proficient in API development (RESTful/GraphQL) and version control (e.g., Git)</li><li>
Expertise in SQL, Python, and databases</li><li>
Familiar with data pipeline tools (e.g., Airflow, dbt) and data warehouse technologies (e.g., Snowflake, Redshift, Databricks)</li><li>
Knowledge of prompt engineering, RAG (Retrieval-Augmented Generation), fine-tuning, and AI agent frameworks</li><li>
Strong knowledge of AI safety, responsible AI practices, and model evaluation techniques</li><li>
Knowledge of US pharma datasets and healthcare concepts is a plus</li><li>
Strong problem-solving, communication, and client-facing skills</li><li>
Proficient in Agile/Scrum and project management tools (e.g., JIRA, Smartsheet)</li><li>
Genuine passion and curiosity for ongoing AI development, with a demonstrated habit of learning and experimenting with new AI tools, models, and techniques</li><li>
Excellent communication and presentation skills (internal and client facing)</li></ul><p><b>
Preferred Qualifications</b></p><ul><li>
Advanced degree(s) (PhD, MD, MBA, or MS) in Computer Science, AI/ML, Data Science, or a related field</li><li>
4+ years of AI/ML engineering experience (AI Engineer) or 6+ years (Senior AI Engineer), including client-facing roles experience</li><li>
Demonstrated ability to integrate business and industry knowledge in developing creative AI solutions</li><li>
Active contributions to open-source AI projects, personal AI projects, or published research in AI/ML</li><li>
Passion for Generative AI and its applications in life sciences and healthcare</li></ul><p><b>
Compensation:</b></p><p>
Base salary range: $100,000 - $150,000 per year. Generous incentive compensation structure.<br>
<br>
Benefits Package:</p><ul><li>
401k - generous employer match with immediate vesting and financial planning resources</li><li>
Comprehensive medical, dental and vision coverage options effective day 1 of employment</li><li>
Flexible spending account (FSA) or Health Savings Account (HSA)</li><li>
Company paid insurances including Short- and Long-Term Disability and Life insurance as well as additional voluntary options</li><li>
Paid parental leave for all new parents</li><li>
Health &amp; Wellness Benefit (e.g., gym memberships; $1000 reimbursement annually)</li><li>
Employee Assistance Program</li><li>
Generous paid time off including vacation, sick days, floating and company holidays</li></ul><div>
Blue Matter is an equal opportunity employer and does not discriminate against otherwise qualified applicants on the basis of race, color, religion, ancestry, age (40 and over), sex (including gender identity, sexual orientation and pregnancy), national origin, disability, marital status, parental status, genetic information, political affiliation, protected veteran status, or any other characteristic protected by law.</div><p>
</p><p>YFln9UxC3u</p></div>

AI Engineer / Senior AI Engineer

eSense

<div>
 Position Overview
 <p>
  <br/>
 </p>
 <p>
  Mid-Level AI Engineer with strong hands-on experience in end-to-end AI system development, specializing in on-premises and private cloud AI deployments. This role focuses on building production-grade AI solutions using primarily local models, optimizing GPU-based training and fine-tuning pipelines, and developing scalable RAG, agentic, multimodal, and speech-enabled systems.
 </p>
 <p>
  <br/>
 </p>
 Key Responsibilities&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
 <p>
  <br/>
 </p>
 <p>
  &middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Analyze requirements and contribute to technical AI solution design.
 </p>
 <p>
  &middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Strong programming skills in Python (mandatory).
 </p>
 <p>
  &middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Experience in backend development (APIs, microservices, model serving).
 </p>
 <p>
  &middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Hands-on experience with local LLM deployment and optimization.
 </p>
 <p>
  &middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Experience in RAG system design and vector databases.
 </p>
 <p>
  &middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Practical experience in model fine-tuning (LoRA / PEFT).
 </p>
 <p>
  &middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Solid understanding of: Transformer architecture, quantization techniques, and embeddings with retrieval.
 </p>
 <p>
  &middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Experience working in Linux environments.
 </p>
 <p>
  &middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Participate in peer code reviews and team planning activities.
 </p>
 <p>
  &middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Provide task estimations and deliver within approved timelines.
 </p>
 <p>
  &middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Apply secure coding practices and participate in bug resolution with quality metrics in mind.
 </p>
 <p>
  <br/>
 </p>
 Qualifications
 <p>
  <br/>
 </p>
 <p>
  &middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Bachelor&rsquo;s degree in computer science, Artificial Intelligence, Data Science, or related discipline.
 </p>
 <p>
  &middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2-5 years of experience in AI Engineering.
 </p>
 <p>
  &middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Proficiency in C#, .NET Core and Python.
 </p>
 <p>
  &middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Experience with Structured and Unstructured databases.
 </p>
 <p>
  &middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Experience with Angular or other frontend technologies is a strong plus.
 </p>
 <p>
  &middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Working knowledge of relational databases (i.e., SQL Server) and non-relational databases (i.e., Qdrant)
 </p>
 <p>
  &middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Familiarity with Git, RESTful services, and Agile development methodologies
 </p>
</div>


Master Works

<div>
 This is a highly skilled Machine Learning Engineer to design, build, deploy, and scale machine learning models that power data-driven products and intelligent systems. This role sits at the intersection of data science, software engineering, and MLOps, and requires strong hands-on experience turning models into production-ready solutions, programming experience in Python or R.
 <br/>
 <br/>
 <ul>
  <li>
   Design, develop, train, and optimize machine learning models for real applications or use cases
  </li>
  <li>
   Translate business and product requirements into scalable ML/AI solutions.
  </li>
  <li>
   Implement feature engineering, model selection, tuning, and evaluation techniques.
  </li>
  <li>
   Develop , and deploy ML models into production environments with high availability and performance
  </li>
  <li>
   Build and maintain ML pipelines (training, validation, deployment, monitoring).
  </li>
  <li>
   Monitor model performance, data drift, and model decay; retrain models as needed.
  </li>
  <li>
   Ensure models meet reliability, scalability, and security standards.
  </li>
  <li>
   Work closely with Data Scientists, Product Managers, and Software Engineers.
  </li>
  <li>
   Collaborate with data engineering teams to ensure high-quality, reliable data pipelines
  </li>
  <li>
   Participate in design and code reviews, ensuring engineering best practices.
  </li>
  <li>
   Optimize models for latency, throughput, and cost.
  </li>
  <li>
   Implement experimentation frameworks (A/B testing, offline evaluation).
  </li>
  <li>
   Apply responsible AI principles, including fairness, explainability, and governance where required
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Requirements
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   3-7+ years of hands-on experience in Machine Learning or applied AI roles
  </li>
  <li>
   Strong programming skills in Python (and/or Java, Scala).
  </li>
  <li>
   Solid understanding of ML algorithms (supervised, unsupervised, deep learning).
  </li>
  <li>
   Experience with frameworks such as TensorFlow, PyTorch, and Scikit-learn.
  </li>
  <li>
   Experience deploying models using Docker, Kubernetes, or cloud ML services.
  </li>
  <li>
   Strong knowledge of data structures, algorithms, and software engineering principles.
  </li>
  <li>
   Experience working in agile, cross-functional teams
  </li>
  <li>
   Experience with cloud platforms (AWS, Azure, or GCP) and managed ML services.
  </li>
  <li>
   Hands-on experience with MLOps tools (MLflow, Kubeflow, Airflow, SageMaker, Azure ML).
  </li>
  <li>
   Experience with big data technologies (Spark, Kafka, Databricks).
  </li>
  <li>
   Background in NLP, Computer Vision, or Generative AI.
  </li>
  <li>
   Strong problem-solving and analytical thinking
  </li>
  <li>
   Production-first mindset
  </li>
  <li>
   Data-driven decision making
  </li>
  <li>
   High Collaboration and communication skills
  </li>
 </ul>
</div>


Scotiabank

<p></p><div><p>Requisition ID: 257554<br>
<br>
Join a purpose driven winning team, committed to results, in an inclusive and high-performing culture.</p><p></p><div><b><br>
Purpose</b></div><br>
<div></div>
<div>
The AI Engineer is an individual contributor responsible for designing, building, and operationalizing enterprise grade AI solutions in a highly regulated banking environment. This role will execute AI engineering and MLOps/LLMOps, ensuring AI solutions are secure, scalable, auditable, and production ready. You will work with Senior Engineers to build and deploy production grade AI systems end to end.</div><br>
<div></div>
<div><b>
What You'll Do</b></div><br>
<div></div>
<div><b>
Production AI Engineering (Build, Deploy, Run)</b></div>
<ul>
<li>Implement production‑grade AI services and pipelines (batch and real‑time) with strong focus on reliability, performance, and operational excellence in the cloud</li>
<li>Execute, manage and support the packaging and deployment of models and solutions as scalable services (APIs, jobs, agents) with clear SLAs, monitoring, alerting, and runbooks.</li>
<li>Own complex problem resolution across environments, including production incidents related to AI systems.</li>
</ul>
<div><b>Governance‑by‑Design (Banking &amp; Regulatory Alignment)</b></div>
<ul>
<li>Embed <b>AI governance directly into engineering workflows</b>, including:
<ul><li>Security and access controls</li>
<li>Data classification and handling</li>
<li>Model risk management requirements</li>
<li>Privacy and consent controls</li>
<li>Responsible AI principles</li>
<li>Auditability and regulatory traceability</li>
</ul></li><li>Partner closely with Risk, Compliance, Legal, and Architecture teams to ensure AI solutions meet internal and external regulatory expectations.</li>
</ul>
<div><b>Generative AI &amp; Advanced AI Capabilities</b></div>
<ul>
<li>Implementation of <b>Generative AI</b> patterns such as Retrieval‑Augmented Generation (RAG), embeddings, semantic search, and agent workflows.</li>
<li>Ensure GenAI solutions are grounded in approved data sources, governed access, logging, and retention policies.</li>
<li>Define evaluation and monitoring approaches for GenAI outputs in regulated use cases.</li>
</ul>
<div><b>Enterprise MLOps / LLMOps
</b></div><ul><li>Implement <b>automated ML/LLM delivery pipelines</b> covering training, evaluation, approval, deployment, and rollback.</li>
<li>Implement standards for model versioning, reproducibility, environment isolation, and controlled releases.</li>
<li>Reduce time‑to‑production while increasing safety, repeatability, and governance through automation.</li></ul><br>
<div></div>
<div><b>
What You'll Bring</b></div><br>
<div></div>
<ul>
<li>Bachelor&rsquo;s degree in Computer Science, Engineering, or equivalent practical experience.</li>
<li>3+ years of experience in cloud engineering, with 3+ years focused on AI/ML systems</li>
<li>Expert‑level proficiency in Python, SQL and cloud infrastructure</li>
<li>Hands‑on experience deploying AI solutions in cloud environments (Azure and GCP).</li>
<li>Deep understanding of production concerns: reliability, scalability, observability, cost, and security.</li>
<li>Experience delivering AI solutions in regulated industries (banking, financial services, insurance, healthcare).</li>
<li>Strong familiarity with model risk management, audit requirements, and regulatory review processes.</li>
<li>Hands‑on experience with enterprise MLOps / LLMOps tooling and platform design.</li>
<li>Experience designing platform‑level AI capabilities, not just individual models.</li></ul><br>
<div></div>
<p><b>
Interested?</b></p><br>
<p></p>
<p>
If your experience is closely related but doesn&rsquo;t align perfectly with every qualification, we do encourage you to apply - you might be the right candidate for this or other roles at Scotiabank!</p>
<p>
At Scotiabank, every employee is empowered to reach their fullest potential, respected for who they are and, embraced for their differences. That&rsquo;s why we work to grow and diversify talent and engage employees in a performance-oriented culture.</p><p><br>
<b>
What's in it for you?</b></p><br>
<p></p>
<p>
Scotiabank wants you to be able to bring your best self to work &ndash; and life, every day. With a focus on holistic well-being, our many flexible benefit programs are designed to help support your unique family, financial, physical, mental, and social health needs.</p><br>
<p></p>
<p>
#Dallas</p><br>
<p></p>
<p>
Location(s): United States : Texas : Dallas<br>
<br>
Scotiabank is a leading bank in the Americas. Guided by our purpose: &quot;for every future&quot;, we help our customers, their families and their communities achieve success through a broad range of advice, products and services, including personal and commercial banking, wealth management and private banking, corporate and investment banking, and capital markets.
<br>
<br>
At Scotiabank, we value the unique skills and experiences each individual brings to the Bank, and are committed to creating and maintaining an inclusive and accessible environment for everyone. If you require accommodation (including, but not limited to, an accessible interview site, alternate format documents, ASL Interpreter, or Assistive Technology) during the recruitment and selection process, please let our Recruitment team know. Candidates must apply directly online to be considered for this role. We thank all applicants for their interest in a career at Scotiabank; however, only those candidates who are selected for an interview will be contacted.</p></div>

NTT DATA

<p><b>Onsite in Warren New Jersey</b><br>
<b>Contract</b><br>
<b>Must be able to work on NTT DATA payroll. No C2C available at this time.</b><br>
<br>
Company Overview:<br>
NTT DATA strives to hire exceptional, innovative and passionate individuals who want to grow with us. If you want to be part of an inclusive, adaptable, and forward-thinking organization, apply now.<br>
<br>
We are currently seeking a AI Engineer to join our team in Warren, New Jersey (US-NJ), United States (US).<br>
<br>
Job Description:<br>
Position Overview<br>
This role sits at the intersection of Generative AI, MLOps, and Intelligent Agent development and is responsible for designing, building, and deploying AI-powered solutions that directly support our P&amp;C insurance operations.<br>
<br>
You will work closely with our data engineering, analytics, and business teams to deliver LLM-powered applications, automated AI agents, and production-ready ML pipelines across claims, underwriting, and actuarial domains. This is a hands-on, delivery-focused role for an engineer who is comfortable moving from architecture whiteboard to working code.<br>
<br>
Key Responsibilities<br>
<b>Generative AI &amp; LLM Engineering</b><br>
<br>
Design, fine-tune, and deploy Large Language Models (LLMs) for insurance-specific use cases including document intelligence, claims summarization, policy interpretation, and underwriting Q&amp;A.<br>
<br>
Build Retrieval-Augmented Generation (RAG) pipelines using vector databases (e.g., Azure AI Search, Pinecone, ChromaDB) to ground LLM outputs in enterprise knowledge bases.<br>
Develop prompt engineering frameworks and systematic evaluation pipelines to ensure LLM output quality, consistency, and safety in regulated insurance contexts.<br>
Integrate LLM capabilities with internal data platforms via LangChain, LlamaIndex, or Semantic Kernel.<br>
Evaluate and benchmark foundational models (OpenAI GPT-4o, Azure OpenAI, Claude, Mistral, Llama) against insurance-specific tasks to guide platform selection.<br>
AI Agents &amp; Automation<br>
Architect and implement autonomous AI agents capable of multi-step reasoning, tool use, and decision-making for workflows such as FNOL triage, claims routing, policy lookup, and compliance checks.<br>
Build agentic frameworks using patterns such as ReAct, Chain-of-Thought, and Tool-Augmented Agents to handle complex, multi-turn insurance workflows.<br>
Design human-in-the-loop (HITL) checkpoints and escalation logic to ensure AI agents operate within defined risk and compliance boundaries.<br>
Integrate agents with internal APIs, data platforms, and enterprise systems using orchestration tools such as Azure Logic Apps, Apache Airflow, or Databricks Workflows.<br>
Develop guardrails, monitoring, and audit logging for all deployed agents to meet regulatory and governance standards.<br>
<br>
<b>MLOps &amp; Model Deployment</b><br>
Build and maintain end-to-end MLOps pipelines covering model training, versioning, validation, deployment, and monitoring using MLflow, Azure ML, and Databricks.<br>
Implement CI/CD pipelines for ML models using Azure DevOps or GitHub Actions, enabling reliable, repeatable model releases.<br>
Deploy models as REST APIs or batch inference services on Azure Kubernetes Service (AKS) or Azure Container Apps, ensuring scalability and low-latency response.<br>
Establish model monitoring frameworks to detect data drift, model degradation, and prediction anomalies in production.<br>
Manage the model registry and lineage tracking to maintain governance and auditability of all AI assets.<br>
Collaborate with data engineering teams to ensure feature pipelines are production-grade, versioned, and integrated with the Feature Store on Databricks or Azure ML.<br>
Collaboration &amp; Delivery<br>
Work closely with business analysts, actuaries, underwriters, and claims professionals to translate domain requirements into AI solution designs.<br>
Participate in Agile/Scrum ceremonies including sprint planning, standups, and retrospectives as an active delivery contributor.<br>
Produce clear, well-structured technical documentation including solution designs, API specs, model cards, and deployment runbooks.<br>
Mentor junior engineers and contribute to internal AI engineering best practices and standards.<br>
<br>
Required Qualifications<br>
Education<br>
Bachelor's degree in Computer Science, Data Science, Machine Learning, Software Engineering, or a related quantitative field. Master's degree is a plus.<br>
<br>
Experience<br>
3 5 years of professional experience in AI/ML engineering, with demonstrated delivery of production-grade AI systems.<br>
Hands-on experience building and deploying LLM-powered applications using frameworks such as LangChain, LlamaIndex, or Semantic Kernel.<br>
Proven experience implementing MLOps pipelines in cloud environments (Azure preferred).<br>
Experience developing AI agents or automation workflows using agentic frameworks.<br>
Prior experience in financial services, insurance, or regulated industries is strongly preferred.<br>
<br>
Technical Skills<br>
Generative AI &amp; LLMs<br>
OpenAI / Azure OpenAI (GPT-4o, GPT-4 Turbo), Claude, Mistral, or open-source LLMs (Llama 3, Falcon)<br>
RAG architectures, vector search, embeddings (OpenAI, Cohere, SentenceTransformers)<br>
LangChain, LlamaIndex, Semantic Kernel<br>
Prompt engineering, few-shot learning, instruction tuning, RLHF concepts<br>
AI Agents &amp; Automation<br>
Agentic frameworks: ReAct, Tool-Augmented Agents, LangGraph, AutoGen, CrewAI<br>
Workflow orchestration: Apache Airflow, Databricks Workflows, Azure Logic Apps<br>
API design and integration: REST, GraphQL, Webhooks<br>
MLOps &amp; Model Serving<br>
MLflow (experiment tracking, model registry, model serving)<br>
Azure Machine Learning, Databricks AutoML &amp; Feature Store<br>
Docker, Kubernetes (AKS), Azure Container Apps<br>
CI/CD: Azure DevOps, GitHub Actions<br>
Model monitoring: Evidently AI, Azure ML monitoring, or equivalent<br>
Programming &amp; Data Engineering<br>
Python (expert level): PyTorch, Hugging Face Transformers, scikit-learn, Pandas, NumPy<br>
PySpark and Delta Lake for large-scale data processing<br>
SQL (T-SQL / Spark SQL) for feature engineering and data validation<br>
Git for version control and collaborative development<br>
Cloud &amp; Platform<br>
Microsoft Azure (Azure OpenAI, Azure AI Search, AKS, Azure Data Factory, Azure Key Vault)<br>
Databricks (Unity Catalog, Delta Live Tables, Workflows)<br>
Microsoft Fabric / OneLake (familiarity a strong plus)<br>
<br>
Preferred Qualifications<br>
Experience with P&amp;C insurance workflows such as FNOL processing, claims triage, underwriting decisioning, or actuarial modeling.<br>
Familiarity with insurance regulatory requirements including NAIC guidelines and data privacy standards (CCPA, GDPR).<br>
Experience implementing responsible AI principles fairness, explainability, and bias mitigation in regulated environments.<br>
Microsoft certifications: Azure AI Engineer Associate (AI-102) or Azure Data Scientist Associate (DP-100) preferred.<br>
Exposure to Data Mesh patterns and publishing AI model outputs as domain data products.<br>
Familiarity with Databricks Model Serving and Mosaic AI capabilities.<br>
<br>
#LI-NorthAmerica<br>
<br>
About NTT DATA:<br>
<br>
NTT DATA is a $30 billion trusted global innovator of business and technology services. We serve 75% of the Fortune Global 100 and are committed to helping clients innovate, optimize and transform for long term success. As a Global Top Employer, we have diverse experts in more than 50 countries and a robust partner ecosystem of established and start-up companies. Our services include business and technology consulting, data and artificial intelligence, industry solutions, as well as the development, implementation and management of applications, infrastructure and connectivity. We are one of the leading providers of digital and AI infrastructure in the world. NTT DATA is a part of NTT Group, which invests over $3.6 billion each year in R&amp;D to help organizations and society move confidently and sustainably into the digital future. Visit us at us.nttdata.com<br>
<br>
NTT DATA endeavors to make https://us.nttdata.com accessible to any and all users. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process, please contact us at https://us.nttdata.com/en/contact-us. This contact information is for accommodation requests only and cannot be used to inquire about the status of applications. NTT DATA is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status. For our EEO Policy Statement, please click here. If you'd like more information on your EEO rights under the law, please click here. For Pay Transparency information, please click here.<br>
<br>
Where required by law, NTT DATA provides a reasonable range of compensation for specific roles. The starting hourly range for this remote role is <b>($75 - 80/hourly )</b>. This range reflects the minimum and maximum target compensation for the position across all US locations. Actual compensation will depend on several factors, including the candidate's actual work location, relevant experience, technical skills, and other qualifications.<br>
<br>
This position is eligible for company benefits that will depend on the nature of the role offered. Company benefits may include medical, dental, and vision insurance, flexible spending or health savings account, life, and AD&amp;D insurance, short-and long-term disability coverage, paid time off, employee assistance, participation in a 401k program with company match, and additional voluntary or legally required benefits.</p>

GE HealthCare

<div><h1 class="jobSectionHeader"><b>Job Description Summary</b></h1>
As an AI Engineer, you will play a critical role in designing, building, and deploying agentic AI systems that reason, plan, act, and learn in complex real world environments. You will develop end to end intelligent agents that orchestrate large language models (LLMs), tools, data sources, and workflows to deliver reliable, production grade automation for clinical and operational use cases.<br>
<br>
You will work at the intersection of AI research, software engineering, and product delivery&mdash;bridging AI capabilities with scalable systems that can operate autonomously, collaborate with humans, and adapt over time within regulated healthcare settings.<br>
<br>
GE Healthcare is a leading global medical technology and digital solutions innovator. Our mission is to improve lives in the moments that matter. Join an organisation where every voice makes a difference, and every difference builds a healthier world.<p></p><h1 class="jobSectionHeader"><b>
Job Description</b></h1><p>
As an AI Engineer, you will play a critical role in designing, building, and deploying <b>agentic AI systems</b> that reason, plan, act, and learn in complex real‑world environments. You will develop <b>end‑to‑end intelligent agents</b> that orchestrate large language models (LLMs), tools, data sources, and workflows to deliver reliable, production‑grade automation for clinical and operational use cases.</p><p></p><p>
You will work at the intersection of AI research, software engineering, and product delivery&mdash;bridging AI capabilities with scalable systems that can operate autonomously, collaborate with humans, and adapt over time within regulated healthcare settings.</p><p></p><p>
GE Healthcare is a leading global medical technology and digital solutions innovator. Our mission is to improve lives in the moments that matter. Join an organisation where every voice makes a difference, and every difference builds a healthier world.</p><p></p><p><b>
Responsibilities</b></p><p></p><p>
Your responsibilities include the following:</p><p></p><ul><li>
Designing and developing <b>agentic AI architectures</b> that combine LLMs with planning, tool use, memory, and feedback loops to automate and augment clinical and operational workflows.</li><li>
Building <b>multi‑step reasoning and decision‑making agents</b> that can decompose tasks, select tools, invoke external systems (e.g. databases, APIs, services), and adapt based on outcomes.</li><li>
Orchestrating AI agents across data modalities including medical images, electronic medical records, waveforms, and clinical reports.</li><li>
Defining evaluation strategies for agent behavior, including task success, robustness, safety, and alignment&mdash;beyond traditional model accuracy metrics.</li><li>
Developing production‑ready systems for <b>agent deployment, monitoring, observability, and lifecycle management</b>, including guardrails, auditing, and human‑in‑the‑loop controls.</li><li>
Building scalable, service‑oriented platforms and reusable components that enable multiple teams to compose, customize, and govern AI agents across products.</li><li>
Prototyping and iterating quickly to validate agent behavior in realistic workflows, then hardening solutions for regulated, high‑reliability environments.</li><li>
Staying current with advances in agentic AI, LLM tooling, orchestration frameworks, and applied AI system design.</li></ul><p></p><p><b>
Basic Qualifications
</b></p><ul><li>Graduate degree in Computer Science or a related field, with two or more years of industry experience.</li><li>
Strong experience in software engineering for AI systems, including designing distributed, stateful, or event‑driven services.</li><li>
Hands‑on experience with <b>LLMs and agent frameworks</b>, such as tool calling, function execution, retrieval‑augmented generation (RAG), memory, and planning patterns.</li><li>
Proficiency in one or more general‑purpose programming languages (e.g. Python, Java, C/C++).</li><li>
Experience working with large‑scale data and production platforms (e.g. Spark, Hadoop, cloud‑native services).</li><li>
Practical experience deploying and operating AI systems in production, including monitoring, logging, and failure handling.</li></ul><p></p><p><b>
Preferred Qualifications</b></p><ul><li>
Experience building <b>autonomous or semi‑autonomous agents</b> in real‑world, noisy, or high‑stakes environments.</li><li>
Familiarity with AI safety, guardrails, interpretability, and human‑in‑the‑loop system design.</li><li>
Experience handling real‑world medical or patient data and working within regulated environments.</li><li>
Background in machine learning or deep learning (e.g. PyTorch, TensorFlow).</li></ul><p></p><p><b>
Eligibility Requirements</b></p><ul><li>
Must be willing to travel to attend meetings, workshops, conferences &amp; etc.</li><li>
Must be willing to work out of an office located in the greater Seattle Area.</li></ul><p></p><p><b>
About Team</b></p><p>
GE Healthcare teams are based in the US (San Ramon, Bellevue), France, Israel (Tel Aviv), and India (Bangalore). This gives us several core overlap hours for shared meetings.</p><p>
Work/Life Balance</p><p>
Our team also puts a significant value on work-life balance. Having a healthy balance between your personal and professional life is crucial to your happiness and success here. We don&rsquo;t focus on how many hours you spend at work or online. Instead, we&rsquo;re happy to offer a flexible schedule so you can have a more productive and well-balanced life&mdash;both in and outside of work.</p><p>
Mentorship &amp; Career Growth</p><p>
We maintain diverse engineering, and leadership perspectives and backgrounds across technology and beyond. Our employees are excited to share their experiences and mentor more junior engineers. Team members are highly encouraged to set up mentorship relationships with seasoned engineers, not only in our team, but also across the broader GE Healthcare population.</p><p>
Inclusive Team Culture</p><p>
Here at GEHC, we embrace our differences. We are committed to furthering our culture of inclusion. We have many employee-led affinity groups, innovative benefit offerings, and encourage ongoing learning experiences. We expect all employees to live and breathe our behaviors: to act with humility and build trust; lead with transparency; deliver with focus, and drive ownership &ndash; always with unyielding integrity.</p><p><b>
About Us</b></p><p>
At GE HealthCare (NASDAQ: GEHC), we see possibilities through innovation. We&rsquo;re partnering with our customers to fulfill healthcare&rsquo;s greatest potential through groundbreaking medical technology, intelligent devices, and care solutions. Better tools enabling better patient care. Together, we are not only building a healthier future but living our purpose to create a world where healthcare has no limits.</p><p>
GE HealthCare offers a great work environment, professional development, challenging careers, and competitive compensation. GE is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age, disability, protected veteran status or other characteristics protected by law.</p><p></p>
We will not sponsor individuals for employment visas, now or in the future, for this job opening. For U.S. based positions only, the pay range for this position is $160,080.00-$240,120.00 Annual. It is not typical for an individual to be hired at or near the top of the pay range and compensation decisions are dependent on the facts and circumstances of each case. The specific compensation offered to a candidate may be influenced by a variety of factors including skills, qualifications, experience and location. In addition, this position may also be eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). GE HealthCare offers a competitive benefits package, including not but limited to medical, dental, vision, paid time off, a 401(k) plan with employee and company contribution opportunities, life, disability, and accident insurance, and tuition reimbursement.<p></p><div><div><div><div><div><div><div><p>
GE HealthCare offers a great work environment, professional development, challenging careers, and competitive compensation. GE HealthCare is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age, disability, protected veteran status or other characteristics protected by law.</p></div></div></div></div></div></div></div><p></p><div><div><div><div><div><div><div><p>
GE HealthCare will only employ those who are legally authorized to work in the United States for this opening. Any offer of employment is conditioned upon the successful completion of a drug screen (as applicable).
</p></div></div></div></div></div></div></div><p></p><p>While GE HealthCare does not currently require U.S. employees to be vaccinated against COVID-19, some GE HealthCare customers have vaccination mandates that may apply to certain GE HealthCare employees.</p><p></p><div><div><div><div><div><div><div><p><b>
Relocation Assistance Provided: </b>Yes</p></div></div></div></div></div></div></div></div><p></p>

Search 10000+ Ai engineer jobs near you this April. New roles added regularly across leading industries. Apply today.

10000 ai engineer jobs Near Me

AI Engineer

AI Engineer / Senior AI Engineer

AI Engineer

AI Engineer

AI Engineer

AI Engineer

AI Engineer