[Remote] Senior AI Automation Engineer
Note: The job is a remote job and is open to candidates in USA. Alignment Health is breaking the mold in conventional health care, committed to serving seniors and those who need it most. The Senior AI & Automation Engineer will architect, build, and scale intelligent AI systems and enterprise automation solutions to improve care quality and operational performance across the Medicare Advantage business.
Responsibilities
- Architect and deliver production-grade AI and machine learning systems
- Lead the design and scaling of intelligent process automation
- Own AI/ML data infrastructure strategy and pipeline reliability
- Integrate AI and automation systems into enterprise architecture
- Architect and implement LLM-powered and agentic AI applications
- Establish reliability standards and drive continuous system improvement
- Mentor engineers and shape AI engineering culture
Skills
- 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
- 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
- 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
- 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 detection, model explainability (SHAP, LIME, or equivalent), and AI governance practices; ability to embed responsible AI standards into team workflows where AI outputs directly influence member care and financial decisions
- 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
- Master's degree in Computer Science, AI/ML, or a related quantitative discipline
- 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
- Cloud certification from AWS, Microsoft Azure, or Google Cloud at an advanced or specialty level (AI/ML or data engineering track)
- RPA platform certification (UiPath, Automation Anywhere, or Microsoft Power Automate)
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