Note: The job is a remote job and is open to candidates in USA. Alignment Health is a healthcare company focused on serving seniors and people with complex medical needs. The AI Automation Engineer will design, build, and deploy AI systems, cloud-native services, integrations, and automated workflows that improve operational efficiency and care quality. The role applies AI/ML, automation, data engineering, and MLOps to areas including claims processing, prior authorization, member communication, and revenue integrity.
Responsibilities
- 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 productionize 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. Contribute to code reviews and documentation, and provide informal mentorship to peers and junior team members through code reviews, documentation, and collaborative problem-solving
Skills
- 3–5 years of professional experience in software engineering with a demonstrated focus on AI/ML, data science, or intelligent process automation
- Hands-on experience building and deploying machine learning models in a production cloud environment (AWS, Azure, or GCP)
- Experience integrating systems with non-API and multimodal data sources
- Experience in a regulated industry (healthcare, insurance, or financial services)
- Hands-on experience applying AI/ML models or LLMs in a production system (integration/serving, not necessarily training)
- Bachelor's degree in Computer Science, Engineering, Mathematics, Data Science, or a related quantitative field
- Equivalent combination of education and demonstrated, progressive hands-on experience will be considered
- Demonstrated proficiency with Python and ML frameworks through professional work experience; formal training or self-directed equivalent accepted
- Working knowledge of cloud AI/ML services (AWS SageMaker, Azure AI, or Google Vertex AI); training or certification equivalent accepted
- Proficiency in Python (primary), with strong command of ML frameworks including PyTorch, TensorFlow, scikit-learn, and Hugging Face Transformers; experience with LLM-based frameworks including RAG pipelines, vector databases, and agentic frameworks such as LangChain or LangGraph
- Hands-on experience with at least one RPA platform (UiPath, Power Automate, Automation Anywhere) and at least one orchestration tool (Apache Airflow, Prefect, or n8n); ability to analyze processes, identify automation opportunities, and estimate ROI
- Hands-on experience with AWS SageMaker, Azure AI / Document Intelligence, Google Cloud Vertex AI, or Databricks; familiarity with containerization (Docker, Kubernetes) and CI/CD pipelines for ML workflows
- Ability to build and maintain ETL and feature pipelines; proficiency with GitHub and model versioning tools (MLflow or equivalent); working knowledge of SQL for data transformation and workflow support
- Working knowledge of HIPAA compliance and data security requirements in regulated healthcare environments; familiarity with healthcare data standards including HL7 FHIR and ICD-10/CPT as applied to AI/ML systems
- Experience integrating AI models and automation services into enterprise applications via REST APIs and microservices; ability to collaborate with software engineering teams to ensure production-grade reliability
- Awareness of and commitment to ethical AI practices including bias detect
- While performing the duties of this job, the employee is regularly required to talk or hear. The employee regularly is required to stand, walk, sit, use hand to finger, handle or feel objects, tools, or controls; and reach with hands and arms
- The employee frequently lifts and/or moves up to 10 pounds. Specific vision abilities required by this job include close vision and the ability to adjust focus
- Experience integrating systems with non-API and multimodal data sources
- Experience in a regulated industry (healthcare, insurance, or financial services)
- Some hands-on experience applying AI/ML models or LLMs in a production system (integration/serving, not necessarily training)
- Master's degree in Computer Science or related quantitative discipline
- Completion of formal certification or coursework in MLOps, cloud AI platforms, responsible AI, or agentic AI development
- Certification in RPA platforms (UiPath, Automation Anywhere, or Microsoft Power Automate)
Benefits
- Fully remote work arrangement
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