Senior Machine Learning Engineer - Generative AI & Full-stack Applications

CVS Health · Healthcare · Springfield, IL +52 · Innovation and Technology

Senior Machine Learning Engineer at CVS Health focused on building and scaling Generative AI and AI-powered capabilities. The role involves implementing AI services, contributing to solution design, building RAG pipelines, implementing guardrails, and ensuring reliability and observability of AI systems. Requires strong software engineering experience and GenAI exposure.

What you'd actually do

  1. Implement AI-powered services and application features that integrate enterprise data and systems with LLMs and ML models.
  2. Build and tune RAG pipelines (retrieval, ranking, grounding) and implement prompt patterns that improve reliability and user experience.
  3. Contribute to evaluation suites and regression testing to ensure quality and safety over time.
  4. Implement guardrails and security controls (input/output validation, policy filtering, and data protection patterns) under guidance from senior engineers.
  5. Instrument services with metrics, traces, logs, and dashboards; participate in incident response and post-incident improvements.

Skills

Required

  • 5+ years of software engineering experience delivering production systems, including modern APIs and application development.
  • 3+ years of experience applying ML/AI concepts in real systems.
  • 3+ years of experience delivering solutions in high-scale, high-availability environments with strong security and compliance requirements.

Nice to have

  • Proficiency in backend engineering (APIs, services) and comfort contributing across the stack when needed.
  • Working knowledge of LLM application development (prompting, RAG, tool calling) and evaluation practices.
  • Experience with CI/CD, testing, containerization, and basic Kubernetes concepts.
  • Strong debugging and operational skills; ability to improve reliability and performance based on telemetry and root cause analysis.
  • Effective collaboration and communication skills across technical and non-technical stakeholders.

What the JD emphasized

  • GenAI exposure (LLMs, RAG, evaluation) strongly preferred
  • delivering solutions in high-scale, high-availability environments with strong security and compliance requirements

Other signals

  • enterprise AI/ML capability
  • GenAI and AI-powered platforms and solutions
  • LLMs and ML models
  • RAG pipelines
  • evaluation suites
  • guardrails and security controls