Staff Machine Learning Engineer - Generative AI & Full-Stack Applications

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.

At CVS Health, our purpose is to deliver better health outcomes by meeting consumers where they are—through local care, digital experiences, and a nationwide team committed to quality, safety, and affordability.

Our Solutions Engineering and Infrastructure organization is building an enterprise AI/ML capability that delivers reliable, responsible, and secure AI-powered platforms and solutions at Fortune 5 scale, and this role is foundational to help us develop that capability.

This is a senior individual-contributor role, focused on identifying evaluating and documenting high-value use cases, designing and prototyping AI-powered solutions, and evolving them into secure, resilient, enterprise-ready products and platform components.

Key Responsibilities:

AI Solution Design & Prototyping

• Partner with stakeholders to identify, evaluate, document, and shape GenAI use cases (copilots, automation, decision support, and insight generation) with clear success metrics.
• Design solution architectures that integrate LLMs with enterprise systems, data sources, and tool/function calling while meeting latency and reliability expectations.
• Develop prototypes rapidly and validate them through evaluation, red-teaming, and user feedback; document tradeoffs and recommendations.

Production Engineering & Enterprise Readiness

• Build production-grade services and full-stack experiences (APIs, UIs, workflows) with secure authentication/authorization, audit logging, and scalable deployment patterns.
• Implement safety, privacy, and compliance controls (e.g., PHI/PII protection, prompt injection defenses, data residency constraints, and policy-based filtering).
• Instrument solutions end-to-end with metrics, traces, logs, and model/app observability; contribute to SLOs, error budgets, and operational runbooks.

Model Enablement & Evaluation

• Build and maintain evaluation harnesses for LLM quality, safety, and business outcomes (offline tests, golden sets, regression suites, and online experiments).
• Implement RAG pipelines (chunking, embedding, vector search, reranking) and optimize for accuracy, cost, and latency.
• Collaborate with platform teams on deployment, monitoring, drift/quality detection, and incident response for model-backed services.

Reusable Components & Engineering Excellence

• Contribute reusable libraries and patterns for prompt management, retrieval, tool calling, and policy enforcement.
• Participate in design reviews and code reviews; mentor senior and mid-level engineers on GenAI engineering practices.
• Continuously improve developer experience through templates, CI/CD automation, and documentation that accelerates safe adoption.

Required Qualifications:

• 7+ years of software engineering supporting Data or AI/ML initiatives, including building and operating production services.
• 3+ years applying ML/AI in production; demonstrated hands-on GenAI delivery (LLMs, RAG, evaluation, and safety controls)
• 3+ years of experience delivering solutions in high-scale, high-availability environments with strong security and compliance requirements.

Preferred Qualifications

• Strong full-stack engineering skills (backend services, APIs, and modern web application development) with a focus on reliability and security.

• Hands-on expertise with LLM application patterns: RAG, tool/function calling, prompt management, evaluation, and guardrails.
• Experience with Python and at least one additional backend language; familiarity with common ML libraries and serving frameworks.
• Working knowledge of containerization and Kubernetes, CI/CD, infrastructure-as-code concepts, and production observability.
• Ability to communicate clearly, influence across teams, and translate business needs into implementable technical plans.

Education:

• Bachelor’s degree or equivalent experience (High School Diploma and 4 years relevant experience)

Pay Range

The typical pay range for this role is:

$106,605.00 - $284,280.00

This pay range represents the base hourly rate or base annual full-time salary for all positions