Staff GenAI Engineer - Healthcare AI Platform

CVS Health; Illinois; Hybride; USD 107 000 - 261 000; Plein temps

Avantages offerts par ce poste

Medical, dental, and vision

Paid time off

Retirement options

Wellness programs

Résumé du poste

CVS Health is seeking an experienced AI/ML Platform Engineer to design, build, and operate GenAI solutions across AWS Bedrock and Google Vertex AI, within HIPAA-regulated environments. The role blends hands-on engineering with architecture leadership, cross-functional collaboration, and scalable data pipelines.

You will mentor engineers, optimize ML workflows, ensure data privacy and governance, and drive production-grade solutions that improve member experiences and healthcare outcomes.

Qualifications

• 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.

Responsabilités

• 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.
• Advices 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.

Connaissances

Large-scale software dev

Solution architecture

Microservices APIs

Node.js

GCP & AWS

Python for AI/ML

Leadership

Formation

Bachelor's degree

Outils

GCP

AWS

Vertex AI

Bedrock

Description du poste

CVS Health is seeking an experienced AI/ML Platform Engineer to design, build, and operate GenAI solutions across AWS Bedrock and Google Vertex AI, within HIPAA-regulated environments. The role blends hands-on engineering with architecture leadership, cross-functional collaboration, and scalable data pipelines.

You will mentor engineers, optimize ML workflows, ensure data privacy and governance, and drive production-grade solutions that improve member experiences and healthcare outcomes.