Staff Software Engineer - Agentic AI & Cloud Solutions

CVS Health

Boston (MA)

On-site

USD 106,605 - 260,590

Full time

14 days+

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Job summary

CVS Health is seeking a Staff Software Engineer focused on developing machine learning solutions and agentic AI/ML. You will lead teams and shape modern AI-driven approaches across Azure, GCP and on‑prem systems, with emphasis on scalable, secure software and enterprise-grade AI platforms.

You will architect intelligent solutions leveraging MCP, RAG, vector databases, and multi‑agent orchestration to automate complex processes and improve decision making while delivering cost-efficient results.

Qualifications

  • 7+ years as Software Engineer with 5+ years leading software development projects
  • 5+ years building cloud-native environments (GCP/Azure/AWS) and front-end tech like Angular
  • 3+ years Python, APIs and microservices architecture
  • 2+ years AI/agentic solution development with LLMs, vector databases and orchestration

Responsibilities

  • Lead project teams across Azure, GCP and on-prem systems
  • Architect and implement complex APIs, microservices and real-time streaming on cloud platforms
  • Guide design of enterprise AI solutions using MCP, RAG and multi-agent workflows
  • Mentor junior engineers and establish technical standards
  • Collaborate across teams to drive AI-driven business outcomes

Skills

Software engineering
ML/AI
Angular
Java Spring Boot
Cloud platforms
APIs/Microservices
Git
LLMs/RAG
Vector databases
CI/CD
Leadership

Education

Bachelor's degree

Job description

CVS Health is seeking a Staff Software Engineer focused on developing machine learning solutions and agentic AI/ML. You will lead teams and shape modern AI-driven approaches across Azure, GCP and on‑prem systems, with emphasis on scalable, secure software and enterprise-grade AI platforms.

You will architect intelligent solutions leveraging MCP, RAG, vector databases, and multi‑agent orchestration to automate complex processes and improve decision making while delivering cost-efficient results.

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