Senior AI Engineer | Onsite

Position Summary

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.

What you will do

• 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.

AI & Cloud Engineering

• 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)

Agentic Frameworks & Automation

• 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).

RAG Architectures

• 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.

Model Lifecycle & Operationalization

• Lead model experimentation, fine‑tuning, evaluation, deployment, and monitoring across cloud platforms.
• Optimize cost, performance, token usage, latency, and scaling for production workloads.

Responsible AI & Compliance

• 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.

Collaboration & Technical Leadership

• 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.

Required Qualifications

• 7+ years in large‑scale software development
• 5+