Note: The job is a remote job and is open to candidates in USA. Sedgwick is hiring a Principal AI Engineer to own technical outcomes for a value-stream pod and build production Generative AI agents, workflows, and applications. The role partners with business and operations teams, sets engineering and AI assurance standards, stewards shared capabilities, and mentors engineers.
Responsibilities
- Owns the technical outcome of a value-stream pod from problem shaping through production operation, including whether work contributes to shared capability or is incomplete
- Partners directly with business transformation leadership, examiners, nurse reviewers, and operations — sitting with users, shaping ambiguous problems, and refusing work that would wire new capability onto a process known to be broken
- Architects and personally builds production Generative AI agents, workflows, and applications, contributing code in Python and React rather than reviewing from a distance
- Sets and enforces engineering standards for the pod: API-first and contract-first delivery, AWS and Terraform as the path to production, GitHub Actions as the CI/CD bar, evaluation and observability as release gates, and human confirmation on consequential claims actions
- Accountable for whether the pod lands on shared substrate (context, documents, agents, assurance) or forks it; exercises stewardship and design-council authority to block duplicate builds
- Defines interface terms with vendor delivery teams when the stream is co-developed: shared repositories in Sedgwick accounts, Sedgwick-owned contracts, shared definition of done
- Establishes evaluation, provenance, and autonomy-ladder evidence for AI workflows — what may auto-complete, what must ask then do, and what is not ready — with the business owning thresholds
- Leads technical solution design and architectural review for the stream; escalates conflicts with enterprise standards (identity, model egress, PHI, portability) rather than absorbing them quietly
- Represents the pod on the AI Engineering design council; operates independently and is expected to converge with peer Principals without requiring SVP arbitration for ordinary technical disagreement
- Provides technical guidance and mentoring to Senior and Junior AI Engineers; raises the pod’s bar on code quality, operational excellence, and forward-deployed practice
- Leads complex troubleshooting, root cause analysis, and performance optimization for business-critical AI services and platforms
- Evaluates and recommends whether a capability should be built in-house, co-developed with a vendor, or not built — with a named owner and a removal path when a compromise is accepted
- Performs other duties as assigned
- Travel as required, including regular on-site work with claims or Managed Care operations and with vendor delivery teams
Skills
- Bachelor's degree in Computer Science, Engineering, or a related quantitative discipline from an accredited college or university
- Ten (10)+ or more years of software engineering experience, including at least four (4) years owning production Generative AI or applied-AI systems in the field — agents, workflows, or applications, including evaluation, failure handling, and a human review loop — or an equivalent combination of education and experience
- Hands-on production experience on AWS, and hands-on Python, React, GitHub Actions, and Terraform, at a level sufficient to set the bar for a pod, not only meet it
- Experience leading technical delivery for a value stream or equivalent without people-management authority
- Expert full-stack engineering skills in Python and React, with strong SQL, APIs, and service design; still writes production code
- Deep, hands-on AWS, Terraform, and GitHub Actions — including identity, networking, environments, and operational recovery — sufficient to set standards and review others' work
- Production command of Generative AI agents, workflows, and applications, including tool use, orchestration, evaluation, provenance, and human-in-the-loop patterns
- Ability to walk into a design session with examiners or operations leaders and leave with a shaped problem, scoped build, and explicit non-goals
- Ability to keep a pod on shared substrate and to stop a duplicate build without escalating every conflict
- Ability to set interface terms with vendors and internal platform teams while remaining a peer IC, not a manager of either
- Strong mentoring, collaboration, and technical communication skills with the ability to influence transformation leadership, architecture, security, and engineering peers
- Ability to meet or exceed Performance Competencies
- Master's degree
- Experience in insurance, claims, healthcare, or another heavily regulated operational domain
- Experience with machine learning, model fine-tuning, and/or graph databases (e.g. Neo4j)
- Experience with PydanticAI (or equivalent agent framework), agent observability, and AI Ops / LLMOps
Benefits
- Remote role open to all U.S. states.
- Work-life balance
- Grow your career with us
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