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 Artificial Intelligence & Automation Engineer is a hands‑on technical contributor on the Data & Technology Solutions team, responsible for designing, building, and deploying intelligent AI systems and automated workflows that drive operational efficiency and elevate the quality of care for our Medicare Advantage members.

You will partner closely with AI Scientists, Data Engineers, Product Managers, Clinical Operations, and Application Engineering teams to translate complex business problems — from claims processing and prior authorization to member communication and revenue integrity — into scalable, production‑grade solutions. This role directly influences our ability to reduce administrative burden, accelerate payment accuracy, and create smarter, faster member experiences.

• Build distributed, cloud-native systems for enterprise workloads.
• Design and build services, APIs, and microservices that run reliably at scale in a regulated environment.
• Apply solid distributed systems fundamentals: fault tolerance, retries and idempotency, queuing and eventing, caching, and horizontal scaling.
• Build hybrid infrastructure that bridges cloud and non-cloud systems.
• Build 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.
• Design for environments where not everything is cloud-native or API-first.
• Build and operate CI/CD, containerization, and deployment pipelines.
• Containerize and deploy using Docker and Kubernetes.
• Build CI/CD pipelines for reproducible, reliable releases, and apply infrastructure-as-code practices across the systems you own.
• Build data and automation pipelines.
• Build ETL and orchestration pipelines using tools such as Airflow or Prefect.
• Automate high-volume, repetitive processes using RPA platforms (e.g., UiPath, Power Automate), with proper error handling, fault tolerance, and alerting.
• Apply AI/ML to production systems (applied, not research).
• Build and serve inferencing pipelines (batch, real-time, streaming) for models handed off from AI Sciences, for workloads such as claims processing, risk adjustment, and member communication.
• Integrate LLMs into workflows using prompt engineering and RAG, and use agentic frameworks to automate multi-step processes.
• This is applied integration and serving work, not model research or training.
• Support the Databricks platform used by AI Sciences.
• Help product ionize models and pipelines built on Databricks, including Delta Lake, MLflow, and Unity Catalog.
• Support CI/CD, orchestration, and access controls that let Databricks-based work move into production.
• Build with cost and reliability in mind.
• Treat cost per inference and infrastructure spend as engineering considerations, not an afterthought.
• Build monitoring, alerting, and drift detection to catch issues before they affect member outcomes.
• Collaborate cross-functionally.
• Partner with AI Scientists, Product Managers, clinical stakeholders, and business analysts to turn requirements into working systems.
• Contr…