ai platform engineer

University of Chicago

<div>
 
 Department
 
 
 
 OPS ITS - Artificial Intelligence and Automation
 
 
 
 
 About The Department
 
 
 
 
 IT Services (ITS) collaborates with campus partners to support the mission of the University of Chicago through the consistent delivery of high-quality solutions and services. We provide secure, stable, and reliable infrastructure and applications that support the mission of the University. ITS supports and enables faculty research and teaching with the effective use of technology, simplifies the technology experience for all users, identifies, manages, and mitigates technology risks for the University.
 
 
 
 
 Job Summary
 
 
 
 
 The AI Platform Engineer provides the operational support of the University of Chicago&rsquo;s enterprise AI services. This position is responsible for the administration, governance support, compliance monitoring, adoption enablement, and workflow development for institutional AI platforms, beginning with Claude Enterprise.
 
 
 This is a hands-on individual contributor role that combines enterprise platform operations with policy support and campus enablement. The position works across organizational boundaries, coordinating with the CISO&rsquo;s office, the Chief Privacy Officer, General Counsel, and academic units to ensure AI services are secure, governed, and delivering institutional value. The role is designed to scale with Claude adoption and the University&rsquo;s AI portfolio. As new AI tools and capabilities emerge, this position supports the institutional intake process for evaluation, governance alignment, and operational integration.
 
 
 
 
 Responsibilities
 
 
 
 
 <ul>
 <li>
 Administers Claude Enterprise: SCIM provisioning lifecycle (onboarding, offboarding, role changes), connector management, feature flag decisions, admin console configuration, and SSO/IdP maintenance.
 </li>
 <li>
 Integrates and operates the Compliance API and audit log exports, including connection to the institutional SIEM where applicable.
 </li>
 <li>
 Monitors platform health, tracks Anthropic&rsquo;s status page and changelog, and evaluates new features and connectors for governance implications before campus enablement.
 </li>
 <li>
 Serves as the Tier 2/3 escalation point for AI platform issues beyond the help desk&rsquo;s scope.
 </li>
 <li>
 Supports the operational relationship with Anthropic&rsquo;s Technical Account Manager, including quarterly reviews, feature requests, and incident escalation.
 </li>
 <li>
 Produces monthly adoption reporting (MAU, connector usage, training completion, support volume) and maintains dashboards for leadership.
 </li>
 <li>
 Supports the faculty governance groups to provide input from the implementation effort and operations, such as prepare meeting materials, track written-response obligations, and process policy recommendations.
 </li>
 <li>
 Supports the institutional intake process for Claude and other AI tool requests (should we buy X?), working with IT Risk to review proposed tools against the governance framework.
 </li>
 <li>
 Maintains the shadow AI spend dashboard and tracks whether the consolidation thesis is delivering projected outcomes.
 </li>
 <li>
 Coordinates with the CISO and Chief Privacy Officer on compliance questions triggered by new AI features, regulatory changes, or platform incidents.
 </li>
 <li>
 Monitors peer institution AI governance developments, state and federal AI legislation, and regulatory guidance relevant to higher education, including FERPA, HIPAA, GLBA,and export controls.
 </li>
 <li>
 Works with the leaders of the training and enablement programs to develop, review and refresh content.
 </li>
 <li>
 Facilitates office hours (faculty and staff sessions) with other support personnel and coordinates Anthropic TAM participation in regular sessions.
 </li>
 <li>
 Works with University Communications to curate and grow the institutional use case library with department-specific examples, prompt templates, and before/after comparisons.
 </li>
 <li>
 Supports the champions network lifecycle through recruitment, engagement, feedback collection, and cohort rotation.
 </li>
 <li>
 Produces new-feature rollout communications when Anthropic releases capabilities that change product features or user workflows.
 </li>
 <li>
 Builds AI-enabled workflows and integrations for campus units using Claude Projects, MCP connectors, and API integrations to solve real operational problems.
 </li>
 <li>
 Prototypes and evaluates emerging capabilities (agentic workflows, Claude Code, new connector surfaces) for institutional applicability.
 </li>
 <li>
 Partners with departmental stakeholders to identify high-value automation opportunities, and translates them into working solutions.
 </li>
 <li>
 Documents workflow patterns and references architectures for campus reuse.
 </li>
 <li>
 Collaborates with device management teams to effectively manage Claude application deployment and management for campus users.
 </li>
 <li>
 Investigates, analyzes and resolves day-to-day technical problems using standard procedures.
 </li>
 <li>
 Works with stakeholders to gather and analyze requirements for developmental programs. Receives a moderate level of guidance to design applications to meet University and business requirements.
 </li>
 <li>
 Performs other related work as needed.
 
 
 </li>
 </ul>
 
 
 Education:
 
 
 
 
 
 Minimum Qualifications
 
 
 
 Minimum requirements include a college or university degree in related field.
 
 
 
 
 Work Experience:
 
 
 
 
 Minimum requirements include knowledge and skills developed through 2-5 years of work experience in a related job discipline.
 
 
 
 
 Certifications:
 
 
 
 
 
 Preferred Qualifications
 
 
 
 
 
 Education:
 
 
 
 
 <ul>
 <li>
 Bachelor&rsquo;s degree in information technology, computer science, information science, public policy, or a related field.
 
 
 </li>
 </ul>
 
 
 Experience:
 
 
 
 
 <ul>
 <li>
 2&ndash;5 years of professional experience in IT operations, platform management, IT governance, or a comparable role that combined technical and policy work.
 </li>
 <li>
 Background drafting or contributing to policy documents, guidance materials, or governance frameworks in a professional setting.
 </li>
 <li>
 Background in higher education IT, academic administration, or a similarly decentralized institutional environment.
 
 
 </li>
 </ul>
 
 
 Technical Skills Or Knowledge:
 
 
 
 
 <ul>
 <li>
 Demonstrated ability to learn enterprise SaaS administration (SSO, SCIM, API integrations, admin consoles) through structured onboarding. Direct prior experience is not required; technical aptitude and a track record of learning complex systems quickly.
 </li>
 <li>
 Familiarity with data governance and compliance frameworks relevant to higher education, including FERPA, HIPAA, GLBA, and export controls.
 </li>
 <li>
 Proficient in enterprise AI platforms, such as Claude, ChatGPT Enterprise, Microsoft Copilot, or comparable, as an administrator or power user.
 </li>
 <li>
 Proficient in identity and access management systems, for example Shibboleth, Entra ID, Okta, or comparable platforms.
 </li>
 <li>
 Familiarity with API integration, scripting, or workflow automation tools.
 
 
 </li>
 </ul>
 
 
 Preferred Competencies
 
 
 
 
 <ul>
 <li>
 Strong written communication skills to produce clear guidance documents, meeting materials, and status reports for both technical and non-technical audiences.
 </li>
 <li>
 Work across organizational boundaries in a decentralized environment, coordinating with stakeholders who do not report to you.
 </li>
 <li>
 Comfortable operating with a moderate level of guidance and direction, with the judgment to escalate complex issues to senior staff as needed.
 </li>
 <li>
 Work with governance committees, advisory boards, or cross-functional working groups.
 </li>
 <li>
 The AI landscape is changing rapidly. This role requires a disposition toward continuous learning and comfort with ambiguity. The platforms, policies, and best practices this person manages will change materially year over year.
 
 
 </li>
 </ul>
 
 Working Conditions
 
 
 
 <ul>
 <li>
 Hybrid, onsite and remote work as determined by business needs.
 </li>
 <li>
 This position reports to the Office of Chief Technology Officer. The CTO provides escalation authority for cross-institutional coordination, such as CISO, General Counsel, Privacy Officer, and academic units.
 </li>
 <li>
 The role is structured with an intensive onboarding period during the initial platform deployment, approximately nine months, during which the engineer will shadow existing IAM and enterprise systems staff to learn platform-specific administration. The workload composition shifts from deployment-heavy to operations-and-development as the platform matures.
 
 
 </li>
 </ul>
 
 Application Documents
 
 
 
 <ul>
 <li>
 Resume/CV (required)
 </li>
 <li>
 Cover Letter (required)
 </li>
 <li>
 References Contact Information (3)(required)
 
 
 </li>
 </ul>
 The University of Chicago uses AI-assisted tools to streamline and augment some recruitment processes; however, AI is not used to make hiring decisions.
 
 
 When applying, the document(s)
 
 MUST
 
 be uploaded via the
 
 My Experience
 
 page, in the section titled
 
 Application Documents
 
 of the application.
 
 
 
 Job Family
 
 
 
 Information Technology
 
 
 
 Role Impact
 
 
 
 Individual Contributor
 
 
 
 Scheduled
 
 
 Weekly Hours
 
 
 
 37.5
 
 
 
 
 Drug Test Required
 
 
 
 
 No
 
 
 
 
 Health Screen Required
 
 
 
 
 No
 
 
 
 
 Motor Vehicle Record Inquiry Required
 
 
 
 
 No
 
 
 
 Pay Rate Type
 
 
 
 Salary
 
 
 
 FLSA Status
 
 
 
 Exempt
 
 
 
 Pay Range
 
 
 
 $120,000.00 - $135,000.00
 
 
 The included pay rate or range represents the University&rsquo;s good faith estimate of the possible compensation offer for this role at the time of posting.
 
 
 
 
 Benefits Eligible
 
 
 
 
 Yes
 
 
 The University of Chicago offers a wide range of benefits programs and resources for eligible employees, including health, retirement, and paid time off. Information about the benefit offerings can be found in the Benefits Guidebook.
 
 
 
 Posting Statement
 
 
 
 The University of Chicago is an equal opportunity employer and does not discriminate on the basis of race, color, religion, sex, sexual orientation, gender, gender identity, or expression, national or ethnic origin, shared ancestry, age, status as an individual with a disability, military or veteran status, genetic information, or other protected classes under the law. For additional information please see the University's Notice of Nondiscrimination.
 
 
 Job seekers in need of a reasonable accommodation to complete the application process should call 773-702-5800 or submit a request via Applicant Inquiry Form.
 
 
 All offers of employment are contingent upon a background check that includes a review of conviction history. A conviction does not automatically preclude University employment. Rather, the University considers conviction information on a case-by-case basis and assesses the nature of the offense, the circumstances surrounding it, the proximity in time of the conviction, and its relevance to the position.
 
 
 The University of Chicago's Annual Security &amp; Fire Safety Report (Report) provides information about University offices and programs that provide safety support, crime and fire statistics, emergency response and communications plans, and other policies and information. The Report can be accessed online at: http://securityreport.uchicago.edu. Paper copies of the Report are available, upon request, from the University of Chicago Police Department, 850 E. 61st Street, Chicago, IL 60637.
</div>

AI Platform Engineer

Lightning AI

<div><div><h2 class="jobSectionHeader">Who We Are</h2>

Lightning AI is the company behind PyTorch Lightning. Founded in 2019, we build an end-to-end platform for developing, training, and deploying AI systems&mdash;designed to take ideas from research to production with less friction.

Through our merger with Voltage Park, a neocloud and AI Factory, Lightning AI combines developer-first software with cost-efficient, large-scale compute. Teams get the tools they need for experimentation, training, and production inference, with security, observability, and control built in.

We serve solo researchers, startups, and large enterprises. Lightning AI operates globally with offices in New York City, San Francisco, Seattle, and London, and is backed by Coatue, Index Ventures, Bain Capital Ventures, and Firstminute.
<h2 class="jobSectionHeader">
The Way We Work</h2>

The people who thrive here are builders who move fast, communicate openly, take ownership, and continuously improve themselves, their teams, and our company. Here's what that looks like in practice:
<ul>
<li>Move with Urgency: We move quickly, make thoughtful decisions, and keep momentum. We value action over perfection and learn by shipping.</li>
<li>Take Ownership: We own outcomes, not just our individual work. We make decisions that move the company forward and follow through.</li>
<li>Communicate Openly: We communicate directly, seek to understand, and create clarity for others. Honest conversations help us move faster together.</li>
<li>Build Great Teams: We lead by example, empower others, and create healthy teams where people can do their best work.</li>
<li>Raise the Bar: We're always improving ourselves. We learn from feedback, consistently challenge ourselves to grow, and focus on the work that matters most.</li>
<li>Think Long-Term: We design for what's next. We create scalable systems, simplify complexity, and use AI and automation to amplify our impact.</li>
</ul>
</div><div></div><h2 class="jobSectionHeader">What We're Looking For</h2>

Lightning AI is looking to hire an AI Platform Support Engineer to join our US Customer Experience team, supporting ML engineers running large-scale training and inference workloads across cloud infrastructure, Kubernetes, and GPU platforms in production environments.

This role sits at the intersection of ML systems, cloud infrastructure, Kubernetes, and customers. You'll support engineers training models, deploying inference systems, and scaling GPU workloads in production.You are not a ticket router or traditional support engineer. You are a technical partner to ML teams - helping diagnose failures, improve reliability, and guide customers through complex distributed systems problems.

The problems range from Kubernetes scheduling and GPU orchestration to distributed PyTorch failures, inference latency, networking bottlenecks, storage performance, and platform reliability. You'll gain exposure to a wide variety of real world AI workloads across industries and help shape the infrastructure powering the next generation of ML applications.

This role is hybrid out of our Seattle, San Francisco, or New York office hubs, with an in-office requirement of at least 2 days per week and occasional team and company offsites. The role follows a Monday&ndash;Friday schedule, with working hours from 8:00 AM to 5:00 PM PST. We are not able to provide visa sponsorship for this role at this time.<h2 class="jobSectionHeader"> 

What You'll Do</h2>

Work Directly With ML Engineers
<ul>
<li>Partner directly with customer engineering teams running training and inference workloads in production</li>
<li>Help customers diagnose and resolve complex distributed systems and ML infrastructure issues</li>
<li>Act as a technical advisor during high impact incidents and platform degradation events</li>
<li>Translate infrastructure level issues into actionable guidance for ML engineers</li>
<li>Build credibility with customers through strong technical reasoning and clear communication</li>
</ul>
Debug ML Infrastructure &amp; Distributed Workloads
<ul>
<li>Investigate failures involving distributed training, Kubernetes orchestration, GPU allocation, networking, and storage systems</li>
<li>Troubleshoot PyTorch, CUDA, NCCL, and inference serving related issues</li>
<li>Analyze logs, metrics, traces, and system behavior to isolate root causes</li>
<li>Debug containerized workloads running across Kubernetes and bare metal GPU environments</li>
<li>Support customers scaling workloads across multi node GPU systems</li>
<li>Diagnose performance bottlenecks involving compute, memory, networking, or storage</li>
</ul>
Improve Reliability &amp; Platform Operations
<ul>
<li>Identify recurring patterns across customer issues and drive long term reliability improvements</li>
<li>Contribute to post incident reviews and operational improvements</li>
<li>Build internal tooling, automation, documentation, and runbooks</li>
<li>Partner closely with infrastructure, networking, and platform engineering teams</li>
<li>Help improve observability, operational visibility, and troubleshooting workflows</li>
<li>Improve the customer experience through better processes and technical guidance</li>
</ul>
What This Role Is Not

To set clear expectations:
<ul>
<li>This is not a traditional help desk or ticket routing support role</li>
<li>This is not purely customer success or account management</li>
<li>This is not a backend engineering role</li>
<li>This is not a passive escalation position</li>
</ul>
This role is for engineers who enjoy solving difficult technical problems while working closely with other engineers. 

<h2 class="jobSectionHeader">
What You'll Need</h2>
<h3 class="jobSectionHeader">Required Qualifications</h3>
<h4 class="jobSectionHeader">Infrastructure &amp; Systems</h4>
<ul><li>Strong software engineering and systems troubleshooting background</li>
<li>Experience with Kubernetes and containerized environments</li>
<li>Linux systems knowledge, including networking, storage, process management, and performance tuning</li>
<li>Experience with cloud infrastructure and distributed systems</li>
<li>Experience with observability and debugging tools such as Prometheus, Grafana, or OpenTelemetry</li>
</ul><h4 class="jobSectionHeader">ML Infrastructure Experience</h4>
<ul><li>Hands on experience operating machine learning workloads in production or research environments</li>
<li>Experience with distributed ML systems and tooling such as PyTorch, CUDA, or NCCL</li>
<li>Familiarity with GPU infrastructure and orchestration</li>
<li>Experience troubleshooting performance, reliability, or scaling issues in ML infrastructure</li>
<li>Understanding of the operational challenges involved in running ML systems at scale</li>
</ul><h4 class="jobSectionHeader">Collaboration</h4>
<ul><li>Strong communication skills and ability to work directly with highly technical customers and engineering teams</li>
<li>Comfortable operating in fast moving, highly ambiguous environments</li>
<li>Enjoys solving complex technical problems collaboratively</li>
</ul><h3 class="jobSectionHeader">Ideal Experience</h3>
<ul><li>Experience with large scale model training or distributed inference systems</li>
<li>Familiarity with Ray, Kubeflow, Slurm, or similar distributed scheduling platforms</li>
<li>Experience with InfiniBand, RDMA, or high-performance networking</li>
<li>Experience operating bare metal infrastructure</li>
<li>Familiarity with storage systems commonly used in ML environments</li>
<li>Experience working at an AI infrastructure, cloud, MLOps, or developer tooling company</li>
<li>Contributions to platform engineering, developer infrastructure, or operational tooling projects</li>
<li>Experience writing automation, tooling, or scripts in Python or similar languages</li>
</ul><div><h2 class="jobSectionHeader">Benefits and Perks</h2>

We offer a comprehensive and competitive benefits package designed to support our employees' health, well-being, and long-term success:
<ul>
<li>Comprehensive Health Coverage: Medical, dental, and vision coverage for employees and eligible dependents.</li>
<li>Meaningful Equity: RSUs that give employees a stake in the company's long-term success.</li>
<li>Retirement Savings: 401(k) matching (U.S.) and pension contributions (U.K.).</li>
<li>Flexible Time Off: Unlimited PTO, company holidays, and floating holidays to support work-life balance.</li>
<li>Company-Wide Winter Break: Two weeks of company closure each winter to disconnect and recharge.</li>
<li>Paid Parental &amp; Family Leave: Paid leave to support you and your family through life's important moments.</li>
<li>Professional Development: Annual learning and development allowance to support your professional growth.</li>
<li>Wellness Benefits: Wellness and work-from-home stipends to support your physical and mental well-being.</li>
<li>Sabbatical Program: Four weeks of paid sabbatical leave after four years of service.</li>
<li>Flexible Work: Flexible schedules and a hybrid work model for our office-based teams.</li>
<li>In-Office Meals: Complimentary meals at our office hubs.</li>
</ul>
Benefits may vary by location, team, and role. 


At Lightning AI, we are committed to fostering an inclusive and diverse workplace. We believe that diverse teams drive innovation and create better products. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other protected characteristic. We are dedicated to building a culture where everyone can thrive and contribute to their fullest potential.</div></div>

AI Platform Support Engineer (US)

Intellias

<div>
 
 
 Tech Lead Engineer &ndash; AI Registry &amp; Discovery Platform (AWS AgentCore)
 
 
 
 
 About the Role
 
 
 
 
 
 
 We are looking for a
 
 Tech Lead Engineer
 
 to design and lead the implementation of an enterprise-grade
 
 AI Registry and Discovery Platform
 
 for AI agents, tools, and MCP services. This role focuses on service registration, metadata governance, semantic discovery, identity mapping, lifecycle management, and integration with the AWS AgentCore ecosystem.
 
 
 As a technical leader, you will drive the architecture and development of a scalable platform that enables secure discovery, governance, and lifecycle management of AI capabilities across the enterprise. You will collaborate with platform, security, and AI engineering teams to establish engineering standards, governance processes, and best practices for enterprise AI platforms.
 
 
 The ideal candidate has strong platform engineering expertise, experience building large-scale registry or catalog systems, and a deep understanding of cloud-native architectures, API security, metadata management, and distributed systems.
 
 
 
 
 
 
 Project Overview
 
 
 
 Our customer is a multinational corporation with more than a century of history and operations in over 180 countries. One of its key strategic initiatives is the development and adoption of a new generation of Reduced-Risk Products (RRPs), targeting more than one billion consumers worldwide.
 
 
 Intellia partners with the customer to engineer a comprehensive software ecosystem supporting innovative IoT products, digital commerce, and enterprise platforms. Our engineering teams develop core platform components powering best-in-class eCommerce, Digital Marketing, and IoT solutions.
 
 
 As a
 
 Tech Lead Engineer
 
 , you will become part of the
 
 Core Architecture Team
 
 , contributing to the design and implementation of a modern Digital Engineering Enterprise Platform.
 
 
 The platform provides engineering teams with reusable services, cloud technologies, governance standards, and operational best practices that accelerate software delivery across more than 700 enterprise applications.
 
 
 
 
 
 
 Responsibilities
 
 
 <ul>
 <li>
 Lead the architecture, design, and implementation of an enterprise AI Registry and Discovery Platform.
 </li>
 <li>
 Design and maintain metadata schemas for MCP servers, AI tools, and agent capabilities aligned with Canonical Contract standards.
 </li>
 <li>
 Build scalable backend services using Python, FastAPI, DynamoDB, and AWS-native technologies.
 </li>
 <li>
 Develop registration, discovery, approval, certification, and lifecycle management workflows for AI agents and MCP services.
 </li>
 <li>
 Design semantic discovery capabilities using vector search, embeddings, and relevance-ranking techniques.
 </li>
 <li>
 Integrate enterprise identity systems and implement Entra Agent ID mapping across AI ecosystems.
 </li>
 <li>
 Implement secure authentication and authorization using OAuth 2.0, JWT, and enterprise identity providers.
 </li>
 <li>
 Design governance processes for onboarding, approval, certification, versioning, deprecation, and retirement of AI services.
 </li>
 <li>
 Develop APIs supporting registry operations, metadata management, discovery services, and governance workflows.
 </li>
 <li>
 Implement secure credential and secret management using HashiCorp Vault and cloud-native security controls.
 </li>
 <li>
 Design observability, auditing, compliance, and operational monitoring capabilities across the registry platform.
 </li>
 <li>
 Collaborate with AI, platform, cloud, and security teams to establish registry standards, governance policies, and engineering best practices.
 </li>
 <li>
 Mentor engineers, provide technical leadership, and support architectural decision-making across the platform.
 </li>
 <li>
 Produce architecture documentation, implementation standards, and technical governance guidelines.
 </li>
 </ul>
 
 
 
 
 
 Requirements
 
 
 <ul>
 <li>
 Bachelor's degree in computer science, Software Engineering, or a related field.
 </li>
 <li>
 5+ years of backend or platform engineering experience.
 </li>
 <li>
 Experience designing and building enterprise-scale registry, catalog, metadata management, or discovery platforms.
 </li>
 <li>
 Strong Python development experience, including Fast API and backend service development.
 </li>
 <li>
 Experience with AWS services, including AWS Agent Core Registry, Gateway, DynamoDB, and related cloud-native technologies.
 </li>
 <li>
 Experience designing metadata schemas and service registration models.
 </li>
 <li>
 Knowledge of semantic search technologies, vector databases, embeddings, and AI-powered discovery.
 </li>
 <li>
 Experience implementing secure API authentication and authorization using OAuth 2.0 and JWT.
 </li>
 <li>
 Experience integrating governance workflows, approval processes, and lifecycle management into enterprise platforms.
 </li>
 <li>
 Experience integrating enterprise identity providers and identity mapping solutions.
 </li>
 <li>
 Strong understanding of cloud-native architecture, distributed systems, API design, and platform engineering.
 </li>
 <li>
 Excellent leadership, communication, mentoring, and stakeholder management skills.
 </li>
 </ul>
 
 
 
 
 
 Nice to Have
 
 
 <ul>
 <li>
 Hands-on experience with AWS AgentCore Registry and Gateway.
 </li>
 <li>
 Experience with vector databases such as pgvector, OpenSearch, or similar semantic search technologies.
 </li>
 <li>
 Experience with HashiCorp Vault or enterprise secrets management platforms.
 </li>
 <li>
 Familiarity with MCP (Model Context Protocol) standards and Canonical Contract schema design.
 </li>
 <li>
 Experience implementing observability and distributed tracing solutions.
 </li>
 <li>
 Knowledge of CI/CD pipelines, Infrastructure as Code (IaC), and cloud security best practices.
 </li>
 </ul>
 
 Why Join Us
 
 <ul>
 <li>
 Lead the development of an enterprise AI Registry and Discovery Platform supporting next-generation AI applications.
 </li>
 <li>
 Work with cutting-edge AWS AgentCore technologies, semantic search, and cloud-native architectures.
 </li>
 <li>
 Drive engineering standards, governance, and technical strategy for enterprise AI platforms.
 </li>
 <li>
 Collaborate with experienced AI, platform, and cloud engineering teams on innovative global-scale solutions.
 </li>
 <li>
 Make a significant impact by shaping the future of AI service discovery, governance, and lifecycle management across a large enterprise.
 </li>
 </ul>
 
 
 
</div>

Python Team Lead – AI Registry & Discovery Platform (AWS AgentCore)

Lowe's Home Improvement

<div>Innovate in Charlotte
Thank you for dedicating your time and talent to Lowe&rsquo;s. We want to give you more opportunities to learn and grow, so if you find a position you&rsquo;re interested in below, we encourage you to apply!
Your Impact 
The primary purpose of this role is to lead artificial intelligence (AI) engineering initiatives and the development of AI solutions that enable the integration and evolution of advanced machine learning (ML) models, including deep learning (DL) frameworks and large language models (LLMs). The Lead AI Engineer will be responsible for leading the advancement of model performance, implementing essential tools and frameworks, and optimizing compute and storage solutions to elevate AI technologies to new heights. 
Work with a Winning Team 
As part of a Fortune 500 company and retail leader, your work can change an entire industry. Our CEO is a forward-thinker when it comes to tech, and with one of Forbes Top 50 CIOs leading the charge, you can come to work knowing you&rsquo;ll have access to the data, tools, and support that few other companies can offer. We also know what it takes to create an inclusive culture that supports you. Our teams are structured around the engineer, giving you the support you need to do your best work. Since we&rsquo;ve been in business for over 100 years, we&rsquo;ve built an excellent track record of growth and success. There&rsquo;s peace of mind knowing you have the stability and resources you need to focus on solving tough challenges. And as you solve these challenges, know you&rsquo;ll be surrounded by supportive associates with curious minds who listen to you, respect you, and recognize your hard work. 
What You Will Do<ul><li>
Leads the development of the AI platform and ensures the scalability of the platform accommodates a wide spectrum of AI models and integration of new AI advancements as APIs.</li><li>
Leads the architectural design, development, and implementation of sophisticated tools and frameworks that elevate ML experimentation and deployment, ensuring that the platform delivers high-end AI functionalities.</li><li>
Architects and orchestrates the management of graphics processing unit and central processing unit resources to enhance the performance of AI models while maximizing cost efficiency and resource utilization.</li><li>
Mentors and guides junior team members to ensure a seamless integration of AI models into the platform.</li><li>
Leads the creation and management of robust data movement strategies and pipelines, optimizing data flows to meet the requirements of intensive AI model training and inference processes.</li><li>
Leads initiatives for continuous improvement by evaluating platform performance analytics and gathering user feedback.</li><li>
Leads cross-functional teams, fostering a collaborative environment that merges deep technical knowledge with strategic business insights and a keen understanding of user needs.</li><li>
Champions the development and enforcement of rigorous security protocols and governance measures for the AI platform, ensuring data integrity and adherence to industry standards and best practices.</li></ul>
Minimum Qualifications<ul><li>
Bachelor&rsquo;s degree in science, technology, engineering, math, or related field or equivalent years of experience in lieu of education requirement, if applicable</li><li>
6 years of experience executing and deploying data science, ML, and DL (fewer years may be accepted with a Master's or Doctorate degree), ML platform engineering, MLOps tools, and ML frameworks</li><li>
6 years of programming experience in Java and/or Python</li><li>
4 years of experience working with cross-functional partners</li></ul> 
Preferred Skills/Education<ul><li>
Master&rsquo;s degree in science, technology, engineering, math, statistics, physics, economics, data science, information science, or quantitative analytics</li><li>
5 years of experience in SQL and NoSQL databases, Hadoop ecosystem, Druid, Trino, Big Query, Google Vertex AI</li><li>
6 years of Python programming experience.</li><li>
2 years of experience in AI technology.</li></ul>
Benefits<ul><li>
401k with up to 4.25% match</li><li>
Discounted Employee Stock Purchase Plan (15% discount of strike price)</li><li>
Tuition-Free Education</li><li>
10-week Maternity/Parental Leave</li><li>
10% Associate Discount</li></ul>
For information about our benefit programs and eligibility, please visit https://talent.lowes.com/us/en/benefits
About Lowe&rsquo;s
Lowe&rsquo;s Companies, Inc. (NYSE: LOW) is a FORTUNE&reg; 100 home improvement company with total fiscal 2025 sales of more than $86 billion. Lowe&rsquo;s employs approximately 300,000 associates and operates over 1,750 home improvement stores, 540 branches and 120 distribution centers. Based in Mooresville, N.C., Lowe&rsquo;s supports the communities it serves through programs focused on creating safe, affordable housing, improving community spaces, helping to develop the next generation of skilled trade experts and providing disaster relief to communities in need. For more information, visit Lowes.com.
Lowe&rsquo;s is an equal opportunity employer and administers all personnel practices without regard to race, color, religious creed, sex, gender, age, ancestry, national origin, mental or physical disability or medical condition, sexual orientation, gender identity or expression, marital status, military or veteran status, genetic information, or any other category protected under federal, state, or local law.
Pay Range: $128,300.00 - $243,800.00 annually Starting rate of pay may vary based on factors including, but not limited to, position offered, location, education, training, and/or experience. For information regarding our benefit programs and eligibility, please visit https://talent.lowes.com/us/en/benefits.</div>

Lead AI Engineer - AI Platform

Huron Consulting Group

<div>Huron is redefining what a global consulting organization can be. Advancing new ideas every day to build even stronger clients, individuals and communities. We&rsquo;re helping our clients find new ways to drive growth, enhance business performance and sustain leadership in the markets they serve. And, we&rsquo;re developing strategies and implementing solutions that enable the transformative change they need to own their future. 
 
As a member of the Huron corporate team, you&rsquo;ll help to evolve our business model to stay ahead of market forces, industry trends and client needs. Our accounting, finance, human resources, IT, legal, marketing and facilities management professionals work collaboratively to support Huron&rsquo;s collective strategies and enable real transformation to produce sustainable business results. 
 
Join our team and create your future.
This person is a shared engineer who builds and runs the platform every pod reuses: the pipelines, testing, deployment and monitoring, plus reusable building blocks so each new pod starts further ahead.<h1 class="jobSectionHeader">
Key Responsibilities</h1><ul><li>
Build and run the pipelines. CI/CD with automated testing across the pods.</li><li>
Provision the environments. Set up and run the environments the pods build in.</li><li>
Deploy and monitor. The models and agents the pods put into production.</li><li>
Set the standards. Technical and security standards every pod builds to.</li><li>
Build reusable parts. So each new pod starts further ahead than the last.</li></ul><h1 class="jobSectionHeader">
Preferred Qualifications</h1><ul><li>
Building internal developer platforms.</li><li>
AI testing and monitoring in production.</li><li>
Healthcare or higher education with data-security awareness (HIPAA, FERPA).</li><li>
AWS Machine Learning or DevOps, Azure AI-102, or Kubernetes (CKA).</li></ul><h1 class="jobSectionHeader">
General Required Skills</h1><ul><li>
5 to 8 years in ML or AI platform work or MLOps, including 1 to 2 years leading.</li><li>
Hands-on running AI systems in production.</li><li>
AWS and Azure, Docker, Kubernetes, infrastructure as code.</li><li>
Strong Python.</li><li>
Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent experience.</li></ul>
The estimated base salary for this job is $140,000 - $170,000 USD. The range represents a good faith estimate of the range that Huron reasonably expects to pay for this job at the time of the job posting. The actual salary paid to an individual will vary based on multiple factors, including but not limited to specific skills or certifications, years of experience, market changes, and required travel. This job is also eligible to participate in Huron&rsquo;s annual incentive compensation program, which reflects Huron&rsquo;s pay for performance philosophy. Inclusive of annual incentive compensation opportunity, the total estimated compensation range for this job is $161,000 - $218,750 USD. The job is also eligible to participate in Huron&rsquo;s benefit plans which include medical, dental and vision coverage and other wellness programs. The salary range information provided is in accordance with applicable state and local laws regarding salary transparency that are currently in effect and may be implemented in the future.
#LI-REMOTE
#LI-CL1<h2 class="jobSectionHeader">
Position Level</h2>
Manager<h2 class="jobSectionHeader">
Country</h2>
United States of America</div>

Platform Engineer, AI Capability Center Hub

FlairsTech

<div><div><div><div><div>Requirements and responsibilities 
<div><h2 class="jobSectionHeader">About the Engagement</h2>
FlareTech is building &quot;Context Intelligence,&quot; an AI platform centered on a proprietary &quot;process neural net&quot; architecture. Rather than relying on standard NLP or data warehouses, the platform builds model graphs that reverse-engineer data models directly from the business, enabling clients to inject knowledge management indexed to their own goals and strategy. FlareTech has secured a Proof of Concept with a major global enterprise client (hundreds of thousands of employees) and needs to accelerate delivery toward an October 1 POC milestone, with full production deployment targeted for November/December.
This role owns the &quot;Context&quot; domain specifically &mdash; context artifacts, ingestion workers, and fine-tuning &mdash; while the existing FlareTech team continues building out process automation and the rest of the platform in parallel.
<h2 class="jobSectionHeader">Main Responsibilities</h2>
<ul><li>Own the Context domain end-to-end: context artifacts, ingestion workers, and the fine-tuning pipeline within the Context Intelligence platform.</li>
<li>Design, implement, and operate production-grade fine-tuning workflows for localized (on-prem / self-hosted) LLMs.</li>
<li>Build and maintain ingestion workers that structure and feed data into the model graph architecture.</li>
<li>Support the reverse-engineering of client data models into model graphs, in line with FlareTech&rsquo;s process-neural-net approach.</li>
<li>Deploy and manage local LLM hosting to meet enterprise confidentiality requirements, as an alternative to cloud-hosted LLM APIs.</li>
<li>Engage directly with Darshan (VP of IT) to scope work, set priorities, and integrate deliverables into the critical path for the October 1 POC.</li>
<li>Coordinate with the existing platform team to ensure the Context domain integrates cleanly with process automation and other platform areas.</li>
<li>Prepare technical documentation on the process neural net / model graphing architecture to support client technical due diligence.</li>
<li>Help accelerate the POC timeline, then transition the Context domain into full production for the November/December deployment.</li>
</ul><h2 class="jobSectionHeader">Required Skills &amp; Experience</h2>
<ul><li>Production experience fine-tuning LLMs &mdash; has shipped fine-tuned models into a live product, not only prompt engineering or inference-time RAG.</li>
<li>Hands-on experience deploying and operating localized / on-prem / self-hosted LLMs (e.g., via vLLM, TGI, Ollama, or comparable serving stacks).</li>
<li>Strong understanding of Retrieval-Augmented Generation (RAG) architectures, and how fine-tuning and RAG complement each other.</li>
<li>Experience building data ingestion pipelines &mdash; workers that clean, structure, and prepare data for model consumption.</li>
<li>Familiarity with graph-based or structured knowledge representations (model graphs, knowledge graphs), including reverse-engineering data models from unstructured or semi-structured sources.</li>
<li>Comfortable operating in a fast-moving, ambiguous R&amp;D environment against a hard external deadline (client POC).</li>
<li>Strong communication skills &mdash; will scope work directly with a client-side VP of IT with minimal intermediary process.</li>
<li>A security- and confidentiality-first mindset, understanding why enterprise clients require local LLM deployment over cloud APIs.</li>
</ul><h2 class="jobSectionHeader">Nice to Have</h2>
<ul><li>Experience building no-code or low-code AI/workflow automation platforms.</li>
<li>Exposure to agentic frameworks or multi-agent systems (useful context, even though this platform favors fine-tuning over complex agent authoring).</li>
<li>Background in enterprise knowledge management or business process automation.</li>
<li>Experience with platform/marketplace business models involving third-party extensibility.</li></ul></div></div></div></div></div></div><div></div>

AI Engineer

Capgemini

<div><div><div><div><div><div><div><h2 class="jobSectionHeader">About Capgemini</h2>
</div><div>Capgemini is a global business and technology transformation partner, helping organizations to accelerate their dual transition to a digital and sustainable world, while creating tangible impact for enterprises and society. It is a responsible and diverse group of 340,000 team members in more than 50 countries. With its strong over 55-year heritage, Capgemini is trusted by its clients to unlock the value of technology to address the entire breadth of their business needs. It delivers end-to-end services and solutions leveraging strengths from strategy and design to engineering, all fueled by its market leading capabilities in AI, generative AI, cloud and data, combined with its deep industry expertise and partner ecosystem. The Group reported 2024 global revenues of &euro;22.1 billion.
</div></div><div><div><h2 class="jobSectionHeader">Job Description:</h2>
</div><div>We're looking for a passionate and experienced AI Platform Engineering Senior Consultant to join our vibrant team! As an AI Platform Engineering Senior Consultant, you will lead the design, implementation, and modernization of enterprise AI platforms supporting Generative AI, Machine Learning, Advanced Analytics, and Intelligent Automation initiatives. You will help customers build secure, scalable, and production-ready AI platforms leveraging NVIDIA AI Enterprise, Kubernetes, OpenShift, VMware, cloud services, and automation technologies across hybrid and multi-cloud environments.
</div></div><div><div><h2 class="jobSectionHeader">Key Responsibilities</h2>
</div><div><ul><li>Lead the architecture, design, and deployment of enterprise AI and Generative AI platforms across cloud, on-premises, and hybrid environments.</li>
<li>Design AI-ready infrastructure leveraging NVIDIA AI Enterprise, GPU technologies, Kubernetes, OpenShift, VMware, and cloud-native services.</li>
<li>Develop AI platform strategies, target-state architectures, governance frameworks, and modernization roadmaps.</li>
<li>Enable enterprise AI use cases including LLMs, RAG, AI Agents, MLOps, LLMOps, and Intelligent Automation platforms.</li>
<li>Implement Infrastructure-as-Code, GitOps, Terraform, Ansible, CI/CD, and automation frameworks to drive platform standardization and operational excellence.</li>
<li>Integrate AI platforms with ServiceNow ITSM, ITOM, CMDB, Change Management, and operational workflows.</li>
<li>Establish monitoring, observability, governance, security, and lifecycle management capabilities for AI platforms.</li>
<li>Lead customer workshops, architecture reviews, executive briefings, solution development, and mentoring activities.</li>
</ul></div></div><div><div><h2 class="jobSectionHeader">Qualifications:</h2>
</div><div><ul><li>Bachelor&rsquo;s degree in Computer Science, Engineering, Information Technology, or a related field.</li>
<li>Minimum 10 years of experience in Platform Engineering, Cloud Architecture, Infrastructure Engineering, AI Platforms, or Technology Consulting.</li>
<li>Minimum 5 years of experience designing and operating enterprise AI, Machine Learning, Data Science, or Advanced Analytics platforms.</li>
<li>Outstanding English communication, both verbal and non-verbal.</li>
<li>Strong expertise in NVIDIA AI Enterprise, GPU-accelerated infrastructure, AI platform architecture, MLOps, and LLMOps.</li>
<li>Deep knowledge of Kubernetes and container platforms including Red Hat OpenShift, AKS, Amazon EKS, GKE, and VMware Tanzu.</li>
<li>Hands-on experience with Terraform, Ansible, GitOps, CI/CD pipelines, and platform automation.</li>
<li>Experience with AI technologies such as PyTorch, TensorFlow, Hugging Face, LangChain, Vector Databases, and Generative AI ecosystems.</li>
<li>Strong understanding of Azure, AWS, and Google Cloud AI services and infrastructure platforms.</li>
<li>Experience integrating enterprise platforms with ServiceNow and implementing governance, security, and operational best practices.</li>
<li>Excellent consulting, stakeholder management, leadership, communication, and presentation skills.</li></ul></div></div></div></div></div></div></div>

AI Platform Engineering Senior Consultant

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