Senior Gen AI Engineer - 100% remote

About the job:

Title: Senior Gen AI Engineer- 100% Remote

Job Type: permanent/direct hire

Notes from call with the hiring manager:

• The hiring manager stated he is looking for Gen AI Engineers who have shipped production Gen AI — not a demo, a hackathon, or a notebook. Full-stack: they have personally shipped both the service and the UI. Candidates who are strong engineers but unwilling to sit with business users, would not be a good fit this opening.
• Excellent greenfield opportunity to help the largest TPA in the world ($21 Billion company) build their AI Platform and utilize GenAI to improve customer experience from the ground up.
• Great work culture
• 100% remote

Required:

• 6–8 years software engineering; 3+ years shipping production Gen AI agents, workflows, or apps (eval, failure handling, human review loop); hands-on AWS, Python, React, GitHub Actions, and Terraform — they have operated what they built, not only consumed a platform someone else ran; has shipped both the service and the interface a business user touches.
• 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

Preferred:

• ML, fine-tuning, graph databases.

Nice to have:

• PydanticAI, agent observability, AI Ops / LLMOps.

Will pass on:

• People managers; research or data-science backgrounds with no production engineering; demo-only Gen AI; deep backend or deep frontend only; Terraform-from-a-template with no ownership; engineers who will not sit with an adjuster or pair with a vendor team.

Primary Purpose:

• The Senior AI Engineer within the Transformation Office owns a build stream end to end — production Generative AI agents, workflows, and applications that examiners, nurse reviewers, and operations actually use.
• This is a full-stack, hands-on role: Python services, React surfaces, AWS, GitHub Actions, and Terraform.
• The Senior AI Engineer has operated what they built, not only consumed a platform someone else ran.
• Operating at the intersection of software engineering and applied AI, the Senior AI Engineer translates shaped business problems into production systems that are secure, observable, evaluable, and aligned to shared substrate rather than one-off solutions.

Responsibilities:

• 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.