Machine Learning Engineer

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.

Design, build, and operationalize scalable, secure, and responsible Generative AI solutions across the affiliate line of business. In this role, you will work across AWS Bedrock
, GCP Vertex AI
, serverless compute, event‑driven architectures, vector search, and agentic frameworks to deliver AI systems that accelerate business outcomes and improve experiences for our members, providers, and colleagues.

This role blends hands‑on engineering with architecture, platform leadership, and cross‑functional collaboration in a HIPAA‑regulated environment.

• Drive the development and implementation of advanced machine learning models and algorithms to solve complex healthcare problems, leveraging techniques such as predictive modeling, deep learning, and natural language processing.
• Collaborate with multiple departments, including data scientists, clinicians, and Information Technology (IT) professionals, to understand business requirements, define machine learning projects, and prioritize initiatives based on strategic objectives.
• Interface with stakeholders to define performance metrics and evaluation methodologies for machine learning models, contributing to rigorous testing, validation, and performance monitoring of models to ensure accuracy and reliability.
• Design and implement scalable and efficient machine learning systems, including data pipelines, preprocessing, feature engineering, and model training, ensuring the quality and integrity of healthcare data used for analysis.
• Advise on the optimization and improvement of data pipelines, model training processes, and infrastructure to enhance efficiency, scalability, and performance of machine learning solutions.
• Consult on and present technical findings, insights, and recommendations to both technical and non‑technical stakeholders, contributing to the dissemination and application of machine learning insights in the healthcare industry.
• Ensure compliance with data privacy regulations, ethical guidelines, and industry standards in machine learning engineering, supporting the development of protocols and practices for model interpretability, fairness, and transparency.
• Manage team performance through regular, timely feedback as well as the formal performance review process to ensure delivery of exceptional services and engagement, motivation, and team development.
• Stay up-to-date with the latest advancements in machine learning and related technologies, continuously exploring and evaluating new algorithms and methodologies to enhance machine learning capabilities in healthcare applications.

• Design, build, and deploy LLM and GenAI applications that are production‑grade using the full breadth of AWS Bedrock and GCP Vertex AI capabilities (models, tuning, pipelines, vector search, guardrails, evaluation).
• Build cloud‑native AI systems using
  • Serverless architectures (AWS Lambda, Step Functions, Event Bridge; Cloud Functions, Cloud Run)
  • Event‑driven architectures (SNS/SQS, Pub/Sub, Event Bridge, triggers)
  • Microservices and APIs (Node.js, Java, Python)

• Architect agentic AI systems using Bedrock Agents, Vertex AI Agent Builder, or approved frameworks (Lang Graph, Lang Chain, Llama Index Agents).
• Deliver multi‑step reasoning, tool‑use, and workflow orchestration for enterprise use cases.
• Comfortably design and develop AI Agents using Copilot Studio and Cloud Flow (Power Automate).

• Develop robust Retrieval‑Augmented Generation (RAG) systems using Bedrock Knowledge Bases, Vertex AI Vector Search, or custom vector databases (Open Search, Pinecone, FAISS, pgvector).
• Design document ingestion, embedding, chunking, grounding, and retrieval…