AI Automation Engineer
About The Position
The AI Automation Engineer is a hands-on technical contributor on Alignment Health’s Data & Technology Solutions team. The role designs, builds, and deploys intelligent AI systems and automated workflows to improve operational efficiency and care quality for Medicare Advantage members. The engineer works with AI Scientists, Data Engineers, Product Managers, Clinical Operations, and Application Engineering to turn business problems—including claims processing, prior authorization, member communication, and revenue integrity—into scalable, production-grade solutions. The position develops cloud-native distributed services and APIs, integrates cloud and non-cloud systems and varied data sources, and builds data, automation, deployment, and inference pipelines. It applies AI and machine learning to production systems, including LLM workflows, while supporting Databricks-based models and pipelines. The work emphasizes reliability, cost, monitoring, responsible production operations, and cross-functional collaboration. This is an individual-contributor role with no direct supervisory responsibility; informal technical mentorship is expected.
Requirements
- 3–5 years of professional software engineering experience 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 such as AWS, Azure, or GCP.
- Hands-on experience applying AI/ML models or LLMs in production systems, including integration or serving.
- Bachelor’s degree in Computer Science, Engineering, Mathematics, Data Science, or a related quantitative field, or an equivalent combination of education and demonstrated progressive hands-on experience.
- Demonstrated professional proficiency with Python and ML frameworks; formal training or a self-directed equivalent is accepted.
- Working knowledge of cloud AI/ML services such as AWS SageMaker, Azure AI, or Google Vertex AI; training or certification equivalent is accepted.
- Proficiency in Python and 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, such as UiPath, Power Automate, or Automation Anywhere, and at least one orchestration tool, such as Apache Airflow, Prefect, or n8n.
- Ability to analyze processes, identify automation opportunities, and estimate ROI.
- Hands-on experience with a cloud AI platform such as AWS SageMaker, Azure AI / Document Intelligence, Google Cloud Vertex AI, or Databricks.
- Familiarity with 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 such as MLflow or equivalent; working knowledge of SQL.
- 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 through REST APIs and microservices.
- Awareness of and commitment to ethical AI practices, including bias detection.
Nice To Haves
- Master’s degree in Computer Science or a related quantitative discipline.
- Formal certification or coursework in MLOps, cloud AI platforms, responsible AI, or agentic AI development.
- Certification in an RPA platform such as UiPath, Automation Anywhere, or Microsoft Power Automate.
- Experience integrating systems with non-API and multimodal data sources.
- Experience in a regulated industry such as healthcare, insurance, or financial services.
- Hands-on experience applying AI/ML models or LLMs in production systems, including integration or serving.
Responsibilities
- Design, build, and operate distributed, cloud-native services, APIs, and microservices for reliable enterprise workloads in regulated environments.
- Apply distributed-systems practices including fault tolerance, retries and idempotency, queuing and eventing, caching, and horizontal scaling.
- Integrate non-API and hybrid data sources, including flat files, legacy databases, EDI feeds, mainframes, streaming data, documents, images, and audio.
- Containerize and deploy applications using Docker and Kubernetes; build CI/CD pipelines and apply infrastructure-as-code practices.
- Build ETL and orchestration pipelines using tools such as Airflow or Prefect.
- Automate high-volume repetitive processes with RPA platforms, including appropriate error handling, fault tolerance, and alerting.
- Build and serve batch, real-time, and streaming inference pipelines for models used in 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.
- Support Databricks-based productionization of models and pipelines, including Delta Lake, MLflow, and Unity Catalog, and support related CI/CD, orchestration, and access controls.
- Monitor cost per inference and infrastructure spend, and build monitoring, alerting, and drift detection to identify issues before they affect member outcomes.
- Collaborate with AI Scientists, Product Managers, clinical stakeholders, and business analysts to translate requirements into working systems.
- Contribute to code reviews and documentation and provide informal technical mentorship to peers and junior team members.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
