Senior Manager- Software Development Engineering-AI

We're building a world of health around every individual - shaping a more connected, convenient and compassionate health experience. At CVS Health®, you'll be surrounded by passionate colleagues who care deeply, innovate with purpose, hold ourselves accountable and prioritize safety and quality in everything we do. Join us and be part of something bigger - helping to simplify health care one person, one family and one community at a time.

AI Launch Pad is CVS Health's centralized AI infrastructure and enablement platform for the HCD Business Unit. We deliver LLM access, observability, and the engineering foundation needed to build and scale production AI agents across the enterprise. As the platform grows, we need a Senior Manager who can simultaneously lead engineering execution, shape the technical roadmap, and influence stakeholders across HCD and the broader CVS organization.

The Senior Manager of AI Launch Pad is a hybrid technical leader and engineering manager responsible for driving the strategy, architecture, and delivery of CVS Health's HCD AI platform. You will lead a team of AI and software engineers, define the platform roadmap, and partner with product, data, security, and business leaders to accelerate enterprise-wide AI adoption. This role requires both deep technical credibility - you can engage in architecture reviews and make sound engineering decisions - and the organizational influence to align cross‑functional teams around a shared AI vision.

• Team Leadership & Development. Recruit, retain, and grow a high‑performing team of AI and platform engineers. Define clear roles, set performance goals, conduct regular 1:1s, and foster a culture of technical excellence, psychological safety, and continuous learning.
• Platform Strategy & Roadmap. Own the AI Launch Pad product and engineering roadmap - balancing immediate delivery commitments with long‑term platform scalability. Prioritize investments in LLM access, observability, agent infrastructure, and developer experience across HCD business units.
• Technical Architecture & Standards. Provide architectural oversight for agentic AI systems, RAG pipelines, LLM evaluation frameworks, and MLOps infrastructure. Define engineering standards and best practices that teams across HCD can adopt to accelerate their own AI initiatives.
• Cross‑functional Stakeholder Management. Partner with HCD product, data, compliance, security, and business unit leaders to align the AI Launch Pad roadmap with enterprise priorities. Translate business needs into platform capabilities and communicate engineering progress and trade‑offs clearly to executive sponsors.
• Delivery & Operational Excellence. Drive end‑to‑end delivery of platform features – from design to production – using Agile/SAFe practices. Establish DORA metrics, CI/CD pipelines, observability standards, and on‑call processes to ensure platform reliability at enterprise scale.
• Enterprise AI Enablement. Accelerate POC‑to‑production cycles across HCD by providing reusable AI building blocks, self‑service tooling, and governance guardrails. Reduce duplication and increase engineering velocity across teams consuming the platform.
• Vendor & Cloud Strategy. Evaluate and manage relationships with cloud providers (GCP, AWS, Azure) and AI vendors. Make informed build‑vs‑buy decisions and ensure the platform remains cost‑efficient and technically current as the AI landscape evolves.
• Compliance & Risk. Ensure all AI systems built on the platform meet HIPAA, security, and regulatory requirements. Partner with Info Sec and Legal to embed privacy‑by‑design and responsible AI principles into the platform foundation.

• 7+ years of software engineering experience, with 3+ years in an engineering management or technical lead role overseeing teams of 4 or more engineers.
• Demonstrated experience architecting and delivering large‑scale, enterprise AI/ML or data platforms – ideally serving thousands of users or multiple business units.
• Technical depth in modern AI/ML infrastructure: cloud‑native architectures (AWS, GCP, or Azure), MLOps, LLM platforms (Bedrock, Vertex AI, or…