Senior AI Automation Engineer

Alignment Health is breaking the mold in conventional health care, committed to serving seniors and those who need it most: the chronically ill and frail. It takes an entire team of passionate and caring people, united in our mission to put the senior first. We have built a team of talented and experienced people who are passionate about transforming the lives of the seniors we serve. In this fast-growing company, you will find ample room for growth and innovation alongside the Alignment Health community. Working at Alignment Health provides an opportunity to do work that really matters, not only changing lives but saving them. Together.

The Senior AI & Automation Engineer is a senior individual contributor and technical leader on the Data & Technology Solutions team, responsible for architecting, building, and scaling intelligent AI systems and enterprise automation solutions that directly improve care quality and operational performance across our Medicare Advantage business. You will serve as a go-to technical expert for AI Scientists, Data Engineers, Product Managers, Clinical Operations, and Application Engineering teams — driving end-to-end delivery of production AI systems that span claims processing, risk adjustment, prior authorization, revenue integrity, and member engagement. This role carries a higher degree of independent ownership and technical authority than the mid-level equivalent, with expectations to lead complex, ambiguous initiatives from concept through production and to actively elevate the engineering practices of the broader team.

• Lead the build of distributed, cloud-native systems for enterprise workloads. Own the design and build of services, APIs, and microservices that run reliably at scale in a regulated environment. Set the standard for distributed systems practice on the team: fault tolerance, retries and idempotency, queuing and eventing, caching, and horizontal scaling.
• Lead hybrid infrastructure work bridging cloud and non-cloud systems. Own the build of integrations with non-API data sources, including flat files, legacy databases, EDI feeds, mainframes, and streaming data, as well as multimodal data such as documents, images, and audio. Set integration patterns the rest of the team builds against.
• Own CI/CD, containerization, and deployment standards. Drive adoption of Docker, Kubernetes, and CI/CD best practices across the team. Own infrastructure-as-code standards for the systems the team builds.
• Own data and automation pipeline reliability. Build and govern ETL and orchestration pipelines at scale. Own automation strategy using RPA platforms and orchestration tools, including ROI governance and engineering standards for fault-tolerant workflow design.
• Apply AI/ML to production systems and own inferencing performance (applied, not research). Own the build of inferencing pipelines (batch, real-time, streaming) that serve models handed off from AI Sciences, for workloads such as claims processing, risk adjustment, and member communication. Partner directly with AI Scientists to take models from research into low-latency, high-throughput production systems, owning the benchmarking and tuning that gets them there. This is applied integration, serving, and performance ownership, not model research or training. Own the technical approach for integrating LLMs into workflows, including prompt engineering, RAG, and multi-agent orchestration frameworks (LangChain, LangGraph, AutoGen).
• Lead the Databricks build-out supporting AI Sciences. Own productionizing models and pipelines built on Databricks, including Delta Lake, MLflow, and Unity Catalog. Own the CI/CD, orchestration, access controls, and cost tuning that let Databricks-based work move reliably into production, and set standards for how the team uses the platform.
• Define alerting, testing, and monitoring frameworks that surface degradation before it affects member outcomes, and lead post-incident reviews.
• Mentor engineers and shape engineering culture. Provide senior technical mentorship through code reviews, design critiques, and paired problem-solving. Contribute to hiring, technical interviews, and team-wide engineering standards, including responsible AI practices such as bias detection, explainability, and governance.

Supervisory Responsibilities 

Senior individual contributor role with no formal direct reports. Expected to function as an informal technical lead: mentoring, conducting code revie…