Staff Software Development Engineer - Tech Lead (Agentic)

Position Summary 

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. 

What You'll Do 

• 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. 

• Knowledge of tools such as Splunk, AppDynamics, LangSmith, Arize.  

• 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. 

Required Qualifications 

• 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. 

Preferred Qualifications 

• 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 

Education 

• Bachelor's degree in Computer Science, Engineering, or equivalent practical experience.