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 reviews, participating in hiring panels, and guiding the technical direction of junior and mid-level engineers on project teams.

Job Requirements

Required

• 5-8 years of professional software engineering experience with a demonstrated focus on distributed systems, cloud infrastructure, or data platforms

• Proven track record independently owning and delivering complex, production-grade distributed systems from concept through deployment and operations

• Experience building and scaling hybrid infrastructure connecting cloud-native and non-API/legacy systems

• Demonstrated experience serving and integrating AI/ML models or LLMs in production, including performance tuning (not model training/research)

• Experience in a regulated industry (healthcare, insurance, or financial services) with deep working knowledge of compliance and security requirements

• Experience building and scaling automation solutions using RPA platforms and workflow orchestration tools, including ROI governance

Preferred:

• Experience with the Databricks platform (Delta Lake, MLflow, Unity Catalog) at a senior or platform-owner level

• Experience applying AI/ML to complex healthcare data (claims, revenue cycle, prior authorization, medical coding, clinical text)

• Familiarity with HL7 FHIR, ICD-10/CPT, or DICOM

• Experience in a Medicare Advantage, managed care, or payer environment at a senior engineering level

• Prior experience as an informal technical lead, principal engineer, or engineering lead

Education:

• Required: Bachelor's degree in Computer Science, Engineering, Mathematics, Data Science, or related quantitative field, or equivalent progressive senior-level experience

• Preferred: Master's degree in Computer Science or related quantitative discipline

License

• Required: None at this time

• Preferred: Advanced or specialty cloud certification (AWS, Azure, or GCP); RPA platform certification

Pay Range: $172,364.00 - $258,547.00
Pay range may be based on a number of factors including market location, education, responsibilities, experience, etc.