Senior AI Engineer

By joining Sedgwick, you'll be part of something truly meaningful. It's what our 33,000 colleagues do every day for people around the world who are facing the unexpected.

As a Senior AI Engineer, you will own a build stream end to end within a pod, designing, building, and operating production Generative AI agents, workflows, and applications.

Responsibilities:

• Owns a build stream end to end within a pod: problem shaping with the Principal, implementation, production operation, and instrumentation of the outcome the business said it would measure.
• Designs, builds, and operates production Generative AI agents, workflows, and applications , including request orchestration, tool execution, state management, and interaction with enterprise systems.
• Develops full-stack software in Python and React, shipping both the service and the interface a business user touches.
• Owns deployment and operation on AWS, with infrastructure as code in Terraform and CI/CD in GitHub Actions, including environments, secrets, and repeatable promotion to production.
• Implements evaluation, observability, and human-in-the-loop controls for AI systems: provenance on material outputs, confirmation on consequential claims actions, and evidence that a workflow is ready to move up the autonomy ladder.
• Implements authentication, authorization, logging, monitoring, and error handling to meet enterprise security, auditability, PHI, and reliability requirements.
• Integrates AI services with enterprise and legacy systems , including APIs, data platforms, and event streams , using approved, non-invasive patterns and versioned contracts.
• Stewards a shared capability as assigned (context, documents, agents, or assurance), with authority to block a duplicate build in another pod.
• Embeds with vendor delivery teams for co-development when the stream requires it: shared repositories in Sedgwick accounts, shared definition of done, not acceptance testing after a demo.
• Sits with examiners, nurse reviewers, or operations staff; watches the work; and turns that into scoped services, workflows, and UI changes.
• Provides technical guidance and mentoring to Junior AI Engineers, promoting engineering standards, code quality, and operational excellence.
• Troubleshoots and leads root cause analysis and performance optimization for latency, throughput, cost, correctness, and reliability of production AI services.
• Evaluates and recommends improvements to architecture, platform capabilities, and engineering process for the stream and the shared substrate.

Additional Responsibilities:

• Performs other duties as assigned.
• Travel as required, including on-site work with claims or Managed Care operations and with vendor delivery teams.

Qualifications:

• Bachelor’s degree in Computer Science, Engineering, or a related quantitative discipline from an accredited college or university required.
• Master’s degree preferred.
• Six (6) + years of software engineering experience, with at least three (3) years shipping production Generative AI agents, workflows, or applications , including evaluation, failure handling, and a human review loop , or an equivalent combination of education and experience.
• Hands-on experience building and operating production workloads on AWS required.
• Hands-on experience with Python, React, GitHub Actions, and Terraform required, including ownership of CI/CD and infrastructure rather than editing templates.

Skills & Knowledge:

• Strong full-stack engineering skills in Python and React, with solid working knowledge of SQL, REST APIs, and service design.
• Hands-on AWS: has built, deployed, and operated production services, including networking, identity, and operational concerns sufficient for a regulated environment.
• Hands-on GitHub Actions and Terraform: owns pipelines and infrastructure as code, including review, drift, and recovery.
• Production command of Generative AI agents, workflows, and applications , tool use, orchestration, evaluation, and human-in-the-loop patterns , sufficient to ship and run, not only prototype.
• Ability to implement services in alignment with enterprise architecture, governance, PHI handling, and compliance requirements.
• Ability to sit with examiners or operations staff and leave with scoped work; comfortable as the only engineer in that room.
• Ability to work collaboratively in a fast-paced Transformation Office, including pairing with vendor engineers when required.
• Strong mentoring, collaboration, and technical communication skills.
• Experience with machine learning, model fine-tuning, and/or graph databases (e.g. Neo4j) preferred.
• Experience with PydanticAI (or equivalent agent framework), agent observability, and AI Ops / LLMOps a plus.