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Tech Lead Full Stack (Backend)

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Summary

Tech lead (backend-heavy full stack) building Java/Spring Boot microservices that power LLM and agentic AI workflows in production — including RAG, tool calling, and MCP integrations — while owning reliability, testing, CI/CD, and end-to-end delivery. 6-10 years of experience required; role is based in India.

Role & responsibilities

  • Develop Java/Spring Boot micro-services that power LLM and agentic workflows in production
  • Build end-to-end application capabilities, integrating backend services and AI workflows with intuitive web-based user experiences.
  • Own production reliability, observability, performance, and unit economics, including latency, token consumption, caching, timeouts, fallbacks, and failure recovery.
  • Build automated unit, integration, API, and end-to-end tests and contribute to CI/CD and production-readiness practices.
  • Work closely with Product, Platform, Security, QA, and Governance teams throughout architecture, design, implementation, and release reviews.
  • Develop agentic workflows using frameworks such as OpenAI Agents SDK, Claude Agent SDK, LangGraph, Spring AI, or LangChain4j, and integrate enterprise tools through MCP and APIs.

Preferred candidate profile

  • Bachelors degree in Computer Science, Engineering, Information Technology, or related technical discipline.
  • 6-10 years of production Java/ Spring Boot, distributed-systems fundamentals: REST/gRPC,
  • Proven experience building end-to-end applications, with deep backend expertise and working knowledge of modern frontend technologies and frameworks.
  • Experience building RAG and agentic AI workflows, including retrieval, tool/function calling, orchestration, context management, and human-in-the-loop patterns.
  • Experience owning applications through the complete engineering lifecyclefrom design and development to CI/CD, deployment, monitoring, troubleshooting, and production support.
  • Experience integrating applications and AI agents with enterprise APIs, tools, data sources, and MCP-based services.
  • AI/ML or Generative AI certifications from recognized cloud or technology providers.
  • Demonstrated continuous learning through technical contributions, open-source projects pr publications


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