Senior AI Automation Engineer

Senior 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 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.

Job Duties / Responsibilities

• Architect and deliver production-grade AI and machine learning systems. Lead the end-to-end design and deployment of predictive and generative AI models — including NLP, classification, regression, and computer vision — for high-stakes Medicare Advantage workloads. Own architectural decisions related to model selection, scalability, and production readiness, and establish monitoring and drift detection standards adopted across the team.
• Lead the design and scaling of intelligent process automation. Evaluate and architect enterprise-wide automation strategies using RPA platforms (e.g., UiPath, Power Automate) and orchestration tools (e.g., Airflow, Prefect). Drive automation ROI analysis, establish engineering standards for fault-tolerant workflow design, and serve as the senior technical owner for the organization's most complex automation pipelines.
• Own AI/ML data infrastructure strategy and pipeline reliability. Design and govern robust ETL and feature engineering pipelines that support model training, validation, and real-time inference at scale. Define infrastructure standards for experimentation, retraining, and monitoring that ensure consistent model performance across a regulated, high-availability production environment.
• Integrate AI and automation systems into enterprise architecture. Lead the integration of deployed models and automation services into enterprise products via REST APIs and microservices, setting the engineering bar for security, HIPAA compliance, and maintainability. Drive adoption of containerization (Docker, Kubernetes) and CI/CD best practices across the AI engineering team.
• Architect and implement LLM-powered and agentic AI applications. Define the technical approach for integrating large language models into clinical and operational workflows — including prompt engineering, fine-tuning, RAG pipelines, and multi-agent orchestration frameworks (LangChain, LangGraph, AutoGen). Own delivery of agentic AI solutions that transform end-to-end workflows in areas such as clinical document intelligence, intelligent prior authorization, and member communication.
• Establish reliability standards and drive continuous system improvement. Own the performance, reliability, and scalability posture of AI and automation systems across the team. Define alerting, testing, and tuning frameworks that proactively surface degradation before it affects member outcomes, and lead post-incident reviews to build organizational resilience.
• Mentor engineers and shape AI engineering culture. Provide senior technical mentorship to mid-level and junior engineers through hands-on code reviews, design critiques, and paired problem-solving. Contribute to hiring, technical interviews, and the establishment of team-wide engineering standards — including responsible AI practices such as bias detection, model explainability, and governance in high-stakes healthcare contexts.

Supervisory Responsibilities

This role is structured as a senior individual contributor with no formal direct reports. The Senior AI & Automation Engineer is expected to function as an informal technical lead — providing mentorship, conducting code reviews, participating in hiring panels, and guiding the technical direction of junior and mid-level engineers on project teams.

Job Requirements

Experience

Required:

• 5–8 years of professional experience in software engineering with a demonstrated, progressive focus on AI/ML, data science, or intelligent process automation
• Proven track record of independently owning and delivering complex, production-grade AI/ML systems from design through deployment and ongoing operations
• Demonstrated experience with the full AI/ML model lifecycle at scale: data architecture, model design, training, validation, deployment, monitoring, and retraining
• Experience in a regulated industry (healthcare, insurance, or financial services) with deep working knowledge of compliance and security requirements in production AI environments
• Experience architecting and scaling automation solutions using RPA platforms and workflow orchestration tools, including cross-functional stakeholder engagement and ROI governance

Preferred:

• Experience applying AI, NLP, or ML to complex healthcare data including claims processing, revenue cycle management, prior authorization, medical coding, or clinical text understanding
• Demonstrated knowledge of healthcare data standards including HL7 FHIR, ICD-10/CPT codes, 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 on an AI/ML team

Education

Required:

• Bachelor's degree in Computer Science, Engineering, Mathematics, Data Science, or a related quantitative field
• Equivalent combination of education and demonstrated, progressive senior-level hands-on experience will be considered

Preferred:

• Master's degree in Computer Science, AI/ML, or a related quantitative discipline

Training

Required:

• Advanced, demonstrated proficiency with Python and ML frameworks through professional work experience at a senior level; formal training or equivalent self-directed mastery accepted
• Hands-on experience with cloud AI/ML platforms (AWS SageMaker, Azure AI, or Google Vertex AI) at an architecture or senior engineering level; certification or equivalent experience accepted

Preferred:

• Formal certification or coursework in MLOps, cloud AI platforms (AWS, Azure, or GCP), responsible AI, or agentic AI development
• Certification in RPA platforms (UiPath, Automation Anywhere, or Microsoft Power Automate)
• Participation in advanced AI/ML communities, conferences, or open-source contributions demonstrating technical depth

Skills & Competencies

Technical / Role-Specific Skills

• AI/ML Engineering: Expert-level Python proficiency with deep command of ML frameworks including PyTorch, TensorFlow, scikit-learn, and Hugging Face Transformers; advanced experience designing LLM-based systems including RAG pipelines, vector databases, and multi-agent orchestration frameworks (LangChain, LangGraph, AutoGen, CrewAI)
• Intelligent Process Automation — Architecture & Governance: Proven ability to architect enterprise-scale automation strategies using RPA platforms (UiPath, Power Automate, Automation Anywhere) and orchestration tools (Apache Airflow, Prefect, or n8n); experience setting engineering standards, conducting automation ROI governance, and leading complex, cross-functional automation programs
• Cloud AI Platforms & MLOps: Expert-level experience with AWS SageMaker, Azure AI / Document Intelligence, Google Cloud Vertex AI, or Databricks; mastery of containerization (Docker, Kubernetes), CI/CD pipelines, and MLOps tooling (MLflow, Kubeflow, or equivalent) at an architecture and governance level
• Data Engineering & AI Infrastructure: Ability to design, govern, and scale ETL and feature pipelines across complex, high-availability healthcare environments; deep proficiency in SQL, data architecture patterns, and real-time inference infrastructure
• Healthcare & Regulatory Expertise: Deep working knowledge of HIPAA compliance, data security requirements, and data governance in regulated healthcare environments; advanced familiarity with healthcare data standards including HL7 FHIR and ICD-10/CPT as applied to production AI and automation systems
• Technical Leadership & AI Innovation: Demonstrated ability to drive architectural decisions, mentor engineers, lead design reviews, and shape engineering standards across a team; track record of identifying and delivering high-ROI AI and automation innovations in an enterprise healthcare context
• Responsible AI & Governance: Applied experience with ethical AI frameworks including bias