Note: The job is a remote job and is open to candidates in USA. CVS Health is building a more connected, convenient, and compassionate health experience through innovation and responsible care. The Staff AI Engineer will design, build, and operationalize scalable, secure Generative AI and machine learning solutions across healthcare business operations, using cloud platforms, agentic frameworks, RAG architectures, and production-grade AI systems. The role also provides architecture leadership, compliance oversight, cross-functional collaboration, and mentorship in a HIPAA-regulated environment.
Responsibilities
- Drives 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
- Collaborates 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
- Interfaces 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
- Designs and implements 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
- Advises on the optimization and improvement of data pipelines, model training processes, and infrastructure to enhance efficiency, scalability, and performance of machine learning solutions
- Consults on and presents 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
- Ensures 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
- Manages 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
- Stays 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 production-grade LLM and GenAI applications 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, EventBridge; Cloud Functions, Cloud Run)
- Event-driven architectures (SNS/SQS, Pub/Sub, EventBridge, triggers)
- Microservices and APIs (Node.js, Java, Python)
- Architect agentic AI systems using Bedrock Agents, Vertex AI Agent Builder, or approved frameworks (LangGraph, LangChain, LlamaIndex Agents)
- Deliver multi-step reasoning, tool-use, and workflow orchestration for enterprise use cases
- Comfortable designing and developing 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 (OpenSearch, Pinecone, FAISS, pgvector)
- Design document ingestion, embedding, chunking, grounding, and retrieval pipelines that integrate securely with enterprise data
- Lead model experimentation, fine‑tuning, evaluation, deployment, and monitoring across cloud platforms
- Optimize cost, performance, token usage, latency, and scaling for production workloads
- Ensure all solutions meet CVS Health’s Responsible AI standards, including model documentation, governance, risk assessment, and auditability
- Design systems that adhere to HIPAA, data privacy, and security requirements
- Partner with product, engineering, data, security, and compliance teams to shape roadmaps and solution direction
- Mentor engineers and contribute reusable patterns, frameworks, and platform accelerators
Skills
- 7+ years in large-scale software development
- 5+ years solution architecture or system design experience
- 5+ years building large-scale microservices/APIs using Node.js
- 3+ years hands‑on experience with GCP and AWS (serverless, event-driven, AI/ML, CI/CD)
- AWS Bedrock (any combination of models, agents, knowledge bases, guardrails, tuning, evaluation) or GCP Vertex AI (Model Garden, Extensions/Agents, Vector Search, Pipelines, Tuning) or similar technologies
- Strong communication and leadership skills
- Strong experience with Python for AI/ML engineering
- Production experience with:
- Agentic AI frameworks
- RAG architectures and vector databases
- Serverless and event-driven designs
- Bachelor's degree, or equivalent experience (HS diploma + 4 years relevant experience)
- Experience with Vertex AI's Generative AI Studio, vector search orchestration, and agent capabilities
- Hands-on experience with vector databases (OpenSearch, Pinecone, FAISS, pgvector, Milvus)
- Strong knowledge of prompt engineering, evaluation, observability, and LLM security patterns
- Ability to write clear design docs, architectural diagrams, runbooks, and technical briefs
- Agile/SAFe experience in large enterprises
- Cloud certifications (AWS/GCP) preferred
Benefits
- Remote work arrangement
- This position is eligible for a CVS Health bonus, commission or short-term incentive program in addition to the base pay range listed above.
- This position also includes an award target in the company’s equity award program.
- Medical, dental, and vision coverage, based on eligibility
- Paid time off, based on eligibility
- Retirement savings options, based on eligibility
- Wellness programs, based on eligibility
- Other resources supporting the physical, emotional, and financial well-being of colleagues and their families, based on eligibility
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