Lead Director - Artificial Intelligence, Machine Learning and Data Engineering

CVS Health•Work At Home-Connecticut, CT

2d•$144,200 - $288,400•Remote

About The Position

CVS Health is seeking a visionary and execution-focused Lead Director - Artificial Intelligence, Machine Learning and Data Engineering to build, scale, and operate enterprise capabilities that enable secure, reliable, responsible, and business-driven adoption of artificial intelligence across one of the largest healthcare organizations in the world. Within the Solutions Engineering and Infrastructure organization, this leader will play a critical role in establishing the foundational data, engineering, platform, governance, and operational capabilities required to deliver artificial intelligence and machine learning solutions at enterprise scale. Reporting to the Executive Director, this leader will oversee teams responsible for developing reusable data products, standing up platforms and components to accelerate AI solution delivery, launch developer enablement accelerators, establish DevSecOps/MLOps/LLMOps operational standards and frameworks for deploying and scaling agentic systems. This role combines deep technical expertise with demonstrated success leading large platform engineering organizations, driving enterprise transformation, delivering platform excellence, and developing high-performing teams. This is a U.S.-based REMOTE position; candidates must reside within the United States.

Requirements

10+ years of experience in software engineering, data engineering, artificial intelligence, machine learning, platform engineering, or related engineering disciplines, including designing and delivering enterprise-scale technology solutions.
7+ years of experience building and leading large-scale platform engineering organizations responsible for enterprise AI/ML platforms, data platforms, products, and technology delivery.
7+ years of hands-on experience building and scaling AI/ML platforms, DevSecOps, MLOps, LLMOps, deployment automation, security engineering, and platform operations supporting mission-critical workloads.
7+ years of experience designing and operating secure cloud-native platforms within highly regulated environments, including privacy, security, governance, compliance, identity management, audit controls, risk management, and operational resilience.
5+ years of experience leading and developing high-performing engineering organizations, including Engineering Managers and senior technical professionals, while building enterprise Data Engineering capabilities, including large-scale data ingestion, batch and streaming architectures, data governance, and reusable data products.

Nice To Haves

Experience leading enterprise AI, Machine Learning, Data Platform, Platform Engineering, or Developer Platform organizations within healthcare, retail, financial services, technology, or other highly regulated industries.
Experience building and governing enterprise AI platforms utilizing Retrieval-Augmented Generation (RAG), GraphRAG, vector databases, foundation models, agentic architectures, MCP frameworks, and responsible AI capabilities.
Demonstrated success managing technology investments, platform portfolios, operating models, financial accountability, strategic vendor relationships, and executive stakeholder engagement.
Experience supporting large-scale platform modernization initiatives involving cloud-native architectures, platform engineering, developer enablement, enterprise data platforms, and AI/ML transformation programs.
Experience leveraging AI-powered engineering tools such as Claude, Claude Code, GitHub Copilot, Microsoft Copilot, and similar technologies to improve software development productivity, platform operations, engineering efficiency, and secure delivery practices.

Responsibilities

Lead the strategy, architecture, and delivery of enterprise AI platform capabilities, including RAG, GraphRAG, vector search, MCP servers, memory and context services, reusable agents, model lifecycle management, and developer enablement accelerators that drive scalable AI/ML adoption.
Lead the strategy, development, and operation of reusable data products and enterprise data engineering capabilities, including data ingestion, transformation, quality, metadata management, lineage, feature engineering, and data services that support AI, analytics, and business outcomes.
Establish enterprise standards and operational practices for platform reliability, observability, infrastructure automation, security, privacy, compliance, model governance, responsible AI, risk management, disaster recovery, capacity planning, and audit readiness.
Partner with Architecture, Cybersecurity, Infrastructure, Product, Data, and Business leaders to guide technology strategy, evaluate emerging technologies, define enterprise standards, optimize investments, reduce duplication, and accelerate AI and data modernization initiatives.
Build, lead, and develop high-performing teams of Engineering Managers, Data Engineers, AI Engineers, Platform Engineers, and Operations Engineers while fostering a culture of innovation, accountability, continuous learning, inclusion, operational excellence, and measurable business impact.