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.

Job Duties / 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.

Supervisory Responsibilities

This is an individual contributor role with no direct supervisory responsibility. The engineer is expected to provide informal technical mentorship to peers and junior team members through code reviews, documentation, and collaborative problem-solving.

Job Requirements

Experience

Required:

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)

Preferred:

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)

Education

Required:

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

Preferred:

Master's degree in Computer Science or related quantitative discipline

Training

Required:

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

Preferred:

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)

Skills & Competencies

Technical / Role-Specific Skills

AI/ML Engineering: 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

Intelligent Process Automation: 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

Cloud AI Platforms: 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

Data Engineering & MLOps: 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

Healthcare & Regulatory Literacy: 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

API Design & System Integration: 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

Responsible AI Principles: Awareness of and commitment to ethical AI practices including bias detect

​Essential Physical Functions:

The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

1. While performing the duties of this job, the employee is