Senior Agentic AI Platform 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.

POSITION SUMMARY

CVS Health is hiring a Senior Agentic AI Platform Engineer to join the AI/ML Solutions Delivery organization and build the foundational platform capabilities that accelerate enterprise-scale adoption of agentic AI solutions. The Senior Agentic AI Platform Engineer will serve as a full-stack platform engineer, designing, developing, and operationalizing reusable components, accelerators, pipelines, frameworks, and agent harnesses that enable product delivery teams to move from prototype to production more efficiently. Leveraging technologies including Google Vertex AI, ServiceNow AI, Databricks, Claude, Python, APIs, and cloud-native services, the Senior Agentic AI Platform Engineer will create secure, scalable, resilient, and high-performing platform capabilities that can be adopted across multiple AI use cases. This role owns the end-to-end lifecycle of shared platform assets and works closely with AI Solutions Architects and Product Delivery teams to ensure platform investments align with business needs, engineering standards, and enterprise governance requirements. The Senior Agentic AI Platform Engineer also provides technical leadership, coaching, and engineering best-practice guidance to contractor resources and interns.

KEY RESPONSIBILITIES

• Design, develop, and maintain reusable platform components, accelerators, pipelines, agent harnesses, and shared services using a combination of pre-built frameworks and custom-developed software across Google Vertex AI, ServiceNow AI, Databricks, and Claude-based ecosystems.
• Build foundational capabilities supporting enterprise AI development, including RAG frameworks, vector-enabled architectures, evaluation frameworks, orchestration services, model integration patterns, and reusable engineering accelerators.
• Own platform capabilities throughout their lifecycle, including architecture, versioning strategy, production hardening, performance optimization, adoption support, and continuous enhancement while making and defending key technical trade-offs.
• Implement secure-by-design platform patterns incorporating AI governance, access controls, data privacy protections, prompt injection safeguards, resiliency mechanisms, failover strategies, and graceful degradation capabilities.
• Partner with AI Solutions Architects, Product Delivery teams, Architecture, Security, and Operations organizations to drive platform adoption, provide onboarding and technical guidance, communicate architectural decisions and trade-offs to engineering teams and senior leadership, and mentor interns and contractor resources.

REQUIRED QUALIFICATIONS

• 5+ years building AI/ML platforms, developer platforms, shared engineering frameworks, or enterprise-scale software solutions with increasing technical ownership and responsibility.
• Proven success over 4+ years developing reusable platform capabilities, accelerators, tooling, or shared services that achieved adoption across multiple engineering teams within enterprise environments.
• 3+ years of hands-on experience building solutions on Google Vertex AI, Azure AI, ServiceNow AI, Databricks, or similar cloud AI platforms supporting production workloads.
• 2+ years designing and implementing retrieval-augmented generation (RAG), agentic AI architectures, vector search solutions, and large language model integrations.
• 2+ years designing secure, scalable, highly available distributed systems while applying AI governance, privacy, resiliency, and operational reliability best practices in fast-paced delivery environments.

PREFERRED QUALIFICATIONS

• Strong full-stack and Python engineering expertise including production-grade development, API and microservice architecture, FastAPI, Docker, pytest, CI/CD practices, and cloud-native application design.
• Experience building agentic AI solutions utilizing Claude or similar large language models, including tool use, function calling, agent orchestration, Model Context Protocol (MCP), and multi-step reasoning patterns.
• Knowledge of prompt engineering, context engineering, retrieval engineering, chunking strategies, embedding models, vector databases such as pgvector, retrieval tuning, reranking, caching strategies, and evaluation frameworks.
• Familiarity with document ingestion and parsing frameworks such as Docling or Unstructured, data engineering practices including SQL, schema migration tooling, ETL pipelines, and bronze, silver, and gold data architectures.

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