Director, AI & Automation Engineering

Alignment Health is committed to transforming the lives of seniors and those who need it most, focusing on providing high-quality care. The Director, AI & Automation Engineering will lead a multi-disciplinary team to define and execute the enterprise AI and automation strategy, improving care quality and operational performance in the Medicare Advantage business.

Responsibilities:

• Set AI & automation strategy aligned to Medicare Advantage priorities. Define and maintain a multi-year AI and automation roadmap that supports strategic goals in Stars, risk adjustment, payment accuracy, prior authorization, clinical quality, and member experience. Partner with C-suite and senior leaders to identify and prioritize high-ROI use cases where AI and intelligent automation create durable competitive advantage
• Lead and develop a high-performing AI & Automation Engineering team. Build, lead, and retain a diverse team of AI/ML engineers, automation engineers, and related roles, setting clear goals, expectations, and development plans. Foster a culture of talent density, accountability, and continuous learning — using data-driven performance management and succession planning to grow future technical leaders
• Drive enterprise-scale intelligent automation. Lead the design and implementation of enterprise-grade automation solutions — including RPA, workflow orchestration, and process intelligence — across functions such as Claims, UM, Provider Operations, and Member Services. Establish standards and governance for automation development, documentation, and change management to ensure scalability and maintainability
• Establish platforms, tooling, and engineering best practices. Own the selection and adoption of AI/ML platforms, cloud services (AWS/Azure/GCP), and automation tools (e.g., UiPath, Power Automate), ensuring they integrate with DTS data platforms and security controls. Define and enforce engineering best practices — CI/CD, MLOps, testing, observability, and documentation — that raise the bar for reliability and time-to-value across all AI and automation initiatives
• Partner cross-functionally to deliver measurable business impact. Collaborate with Product, Data Engineering, Clinical Operations, Finance, Compliance, and Security to translate business requirements into technical solutions and track realized value (e.g., cycle time reduction, accuracy improvement, cost-to-serve). Regularly communicate AI and automation strategy, progress, and ROI to senior executives and governance bodies
• Embed responsible AI and regulatory compliance. Work with Legal, Compliance, and Privacy to ensure all AI and automation solutions meet CMS, HIPAA, and internal governance standards. Implement frameworks for bias detection, model explainability, auditability, and change control, recognizing that AI outputs directly influence member care decisions and financial outcomes
• Manage budget, vendors, and external partnerships. Own the operating budget for AI & Automation Engineering, including cloud spend, third-party platforms, and external partners. Evaluate and manage vendor relationships to ensure investments reflect labor market realities and maximize ROI

Requirements:

• 8–12 years of progressive experience in software engineering, data science, or AI/ML roles, with at least 3–5 years in a senior leadership or director-level position
• Proven track record of delivering production-grade AI/ML systems and large-scale automation solutions in a regulated or enterprise environment
• Demonstrated experience leading and scaling multi-disciplinary engineering teams, including hiring, performance management, and career development
• Deep expertise in machine learning, NLP, generative AI (LLMs, RAG pipelines), agentic frameworks, and intelligent process automation (RPA and orchestration)
• Experience managing budgets, vendor relationships, and technology platform decisions at a department or function level
• Bachelor's degree in Computer Science, Computer & Electrical Engineering, Mathematics, Data Science, or a related quantitative field
• Equivalent combination of education and demonstrated, progressive senior leadership experience will be considered
• Demonstrated senior-level proficiency with AI/ML engineering practices, cloud platforms, and enterprise automation through professional experience; formal training or equivalent self-directed mastery accepted
• Working knowledge of cloud AI/ML governance and platform management (AWS, Azure, or GCP) at a leadership level; certification or equivalent experience accepted
• AI/ML Strategy & Technical Governance: Deep, hands-on understanding of the full AI/ML lifecycle — model design, training, deployment, monitoring, and MLOps — with the ability to set organizational standards, make high-stakes platform decisions, and guide senior engineers through complex technical challenges
• Intelligent Automation Leadership: Proven ability to define and govern enterprise automation strategy using RPA platforms (UiPath, Power Automate, Automation Anywhere) and orchestration tools (Airflow, Prefect); experience establishing automation governance frameworks, ROI accountability, and cross-functional program leadership
• Cloud AI Platform Ownership: Expert-level familiarity with AWS SageMaker, Azure AI / Document Intelligence, Google Cloud Vertex AI, or Databricks; experience owning platform selection, cost governance, and architecture standards at a department level
• Engineering Leadership & Talent Development: Demonstrated ability to build, scale, and retain high-performing AI engineering teams; strong track record in performance management, succession planning, and cultivating a culture of technical excellence and continuous learning
• Healthcare Domain & Regulatory Expertise: Deep working knowledge of HIPAA, CMS regulations, and data governance in regulated healthcare environments; advanced fluency in healthcare AI use cases including risk adjustment, payment integrity, clinical NLP, and member engagement
• Executive Communication & Stakeholder Influence: Ability to translate complex AI and automation strategy into clear executive narratives; experience presenting to boards, C-suite, and governance bodies; demonstrated success influencing senior decision-making through data, business outcomes, and technical credibility
• Responsible AI & Governance: Applied leadership experience with ethical AI frameworks including bias detection, model explainability (SHAP, LIME, or equivalent), auditability, and responsible AI program design; ability to embed governance standards organization-wide in a high-stakes healthcare context
• Experience in healthcare — specifically Medicare Advantage, managed care, or payer environments — with fluency in use cases such as risk adjustment, prior authorization, claims processing, Stars ratings, and revenue cycle management
• Demonstrated knowledge of healthcare data standards including HL7 FHIR, ICD-10/CPT, or DICOM as applied to AI systems
• Track record of building and scaling an AI or automation engineering function from early stage to enterprise maturity
• Published research, conference presentations, or demonstrated thought leadership in AI/ML or enterprise automation
• Master's degree or PhD in Computer Science, AI/ML, or a related quantitative discipline
• Executive or senior-level certification or coursework in AI strategy, responsible AI governance, MLOps, or cloud AI platforms
• Certification in RPA platforms (UiPath, Automation Anywhere, or Microsoft Power Automate) or advanced MLOps tooling
• Participation in AI/ML industry bodies, advisory boards, or standards organizations
• Advanced or specialty cloud certification from AWS, Microsoft Azure, or Google Cloud (AI/ML, data, or architecture track)
• RPA platform certification at an advanced or architect level (UiPath, Automation Anywhere, or Microsoft Power Automate)