Staff Software Development Engineer - Tech Lead (Agentic)

CVS Health•Work At Home-Massachusetts, MA

3d•$130,295 - $260,590

About The Position

We are seeking a hands-on Tech Lead / Staff Software Development Engineer to join a high-energy team driving AI transformation across our product platform. This role will be involved throughout the entire development lifecycle — designing and building AI-powered application capabilities that empower engineering teams to develop, deploy, and operate agentic AI applications at scale. The role spans the full stack: agent orchestration and backend services in Python and Java, APIs, and front-end interfaces in Angular, using LangGraph for agent orchestration across cloud-native infrastructure. You'll be equally comfortable in the agent logic, the API layer connecting it to the product, and the React/TypeScript frontend that renders it — and you'll set the technical direction for how the rest of the team builds all three. This role requires the ability to deliver efficient, production-grade solutions while identifying and mitigating risk before deployment. The ideal candidate brings strong full-stack engineering fundamentals, a track record of technical leadership and peer mentorship, and a genuine interest in using agentic AI to solve real product problems — not just prototype them.

Requirements

8+ years of professional software engineering experience, with demonstrated full-stack ownership (backend services, APIs, and frontend interfaces). Must have taken several Web / agentic applications to production
2+ years hands-on experience building and operating LLM/agent-powered applications in production — real users, real load, real failure modes, not just a prototype.
Hands-on experience with CopilotKit (or comparable in-app agent UI frameworks) — shared state, generative UI, human-in-the-loop patterns, frontend action/tool wiring.
Experience building or operating Deep Agents-style systems: multi-step planning, sub-agent orchestration, long-running or asynchronous agent tasks.
Practical experience with LangSmith (or equivalent) for tracing, evaluation, and debugging LLM/agent behavior in production.
Hands-on experience with LangGraph for agent orchestration — building, debugging, and scaling multi-step agent graphs in production.
Strong, current web technologies experience — React, modern component architecture, browser performance, and accessibility — paired with backend skills in Java and Python, and experience designing APIs that connect the two layers cleanly.
Demonstrated technical leadership: design reviews, mentoring, and being the escalation point when things break in production.
Experience working on a platform team — building capabilities and tooling that other engineering teams build on top of, not just single-product feature work.
Proven ability to manage and context-switch across multiple concurrent projects and stakeholders without dropping the details.
Strong communication skills, written and verbal — this role requires translating technical tradeoffs for engineers, product, and leadership alike, and communication is treated as a core competency, not a soft nice-to-have.
Out-of-the-box thinker who can move quickly — comfortable making sound calls with incomplete information and iterating rather than waiting for the perfect plan.
Solid grasp of LLM fundamentals: prompt engineering, RAG, tool/function calling, context management, and the failure modes specific to non-deterministic systems.

Nice To Haves

Familiarity with the AG-UI protocol or other agent-to-frontend interaction standards.
Experience with additional agent frameworks (CrewAI, Mastra, Pydantic AI, or similar) and a clear point of view on the real tradeoffs between them.
Experience with vector databases and retrieval pipelines (Pinecone, pgvector, Weaviate, etc.).
Background in observability/platform engineering outside of AI (tracing, monitoring, incident response).
Model fine tuning
ML Ops
Traditional ML /Data Science experience

Responsibilities

Architect, design and build full-stack AI application capabilities — spanning agent backend services, APIs, and front-end interfaces — that enable engineering teams to ship agentic features reliably.
Design and build full-stack AI application capabilities — spanning agent backend services, APIs, and front-end interfaces — that enable engineering teams to ship agentic features reliably.
Lead technical design for agent-powered product features using CopilotKit for in-app copilots and generative UI, and Deep Agents-style architectures (planning, sub-agent delegation, long-horizon task execution) for complex workflows.
Instrument, trace, and evaluate agent behavior in production using LangSmith, driving down latency, cost, and error rate through systematic evaluation rather than guesswork.
Own the connective tissue between backend agent logic and frontend experience — API design, state synchronization, and human-in-the-loop approval flows.
Review and influence designs produced across the team; set and enforce engineering standards for agent reliability, prompt/version management, and rollback strategies.
Mentor senior and mid-level engineers on agentic systems specifically — most engineers haven't built these before; you have.
Drive platform scalability, performance, and reliability as usage grows — cost controls, concurrency, multi-tenant isolation, and infra choices that hold up under real production load. Experience with building, deploying and working hands on with Kubernetes environments
Support production issues across the full stack — frontend, backend, infra, and agent behavior alike: investigate, root-cause, and close the loop with tests, evals, and monitoring so the same failure doesn't recur.
Build and maintain modern web technologies across the product: performant, accessible React/TypeScript interfaces, well-designed REST/GraphQL APIs, and the infrastructure that serves them at scale.
Partner directly with product and design to translate ambiguous "make the agent do X" requests into scoped, testable technical plans.