TECHNICAL LEAD - Stacks

Job description

**Job Title: Technical Lead (JAVA + React  + Agentic AI)** **Experience:** Min 8 + Years **Location :** Bangalore/Noida/Hyderabad/Pune **NP** : Immediate Joiner or Serving who can join us within 15 days **Mandatory Skills: JAVA, Spring boot, Microservices, Kafka, Design Patterns, React,  AI Agent orchestration (LangChain, LangGraph), LLM systems, RAG, Vector Databases (PgVector), Python, FAST API, Azure** **Key Responsibilities:** 1. **Design and develop Agentic AI systems capable of reasoning, planning, and executing complex workflows using Large Language Models.** **2. Build AI-powered services using LLM APIs such as OpenAI, Azure OpenAI Service, or other foundation model providers.** **3. Develop and orchestrate AI agents using frameworks such as LangChain, LangGraph, and LlamaIndex.** **4. Design and implement multi-agent systems, including agent collaboration, task decomposition, and tool usage.** **5. Build Retrieval-Augmented Generation (RAG) pipelines integrating enterprise knowledge sources.** **6. Integrate vector databases such as PgVector, Pinecone, Weaviate, or Milvus to enable semantic search and knowledge retrieval.** **7. Build scalable backend services using Java (Spring Boot / Netflix DGS) for enterprise integrations and high-throughput APIs.** **8. Write Python services using Object-Oriented design principles to support LLM orchestration, prompt engineering, and agent execution.** **9. Develop AI microservices using FastAPI to expose agent capabilities and LLM-powered workflows.** **10. Integrate AI agents with enterprise systems via REST APIs, event streams, and databases.** **11. Design and implement tool integrations enabling AI agents to interact with internal services, APIs, and automation workflows.** **12. Implement memory architectures for AI agents including short-term memory, long-term knowledge retrieval, and context management.** **13. Design observability, monitoring, and evaluation frameworks to measure LLM performance, agent behaviour, hallucination rates, and task success.** **14. Optimize prompt engineering, model selection, token usage, latency, and cost efficiency.** **15. Build guardrails and safety mechanisms for reliable AI system behaviour.** **16. Design, develop, and deploy AI services on Microsoft Azure, leveraging services such as Azure OpenAI, Azure Functions, Azure Kubernetes Service (AKS), and related cloud services.** **17. Design and run evaluation pipelines and experimentation frameworks to continuously improve AI agent accuracy, reliability, and performance.** **18. Collaborate with product managers, and engineering teams to translate business problems into AI-driven solutions.** **Required Skills** - **Design and develop modern, scalable front-end applications using React and TypeScript, delivering intuitive interfaces for AI-driven workflows, multi-agent interactions, and complex task orchestration dashboards.** - **Real-time Response handling as streaming chat responses, token-by-token updates, agent tool traces, and live execution timelines- using WebSocket, Socket.IO or Server-Sent Events (SSE).** - **Develop front-end components that visualize agentic AI systems, including reasoning steps, tool invocations, graphs and planning timelines.** - **Implement advanced chat UI patterns for LLM experiences: markdown rendering, citations, code blocks, memory visualizers, context inspectors, and interactive prompt builders.** - **Build RAG-aware UI components that highlight retrieved chunks, knowledge sources, confidence scores, semantic matches, and dynamic grounding of answers.** - **Integration of backend AI services via REST, GraphQL, WebSocket, and streaming endpoints to support complex workflows, agent execution states, and continuous output rendering.** - **Develop state management architecture using Redux Toolkit, Zustand or React Query, optimized for real-time data flows and high-frequency updates from AI systems.** - **Implement front-end performance optimizations including lazy loading, Suspense, memorization, virtualization, and streaming-friendly rendering strategies to support low-latency AI UX.** - **Build reusable design systems and UI component libraries based on Atomic design patterns.** - **Secure the front-end application with best practices around XSS protection, content sanitization, secure storage, authentication flows, and CSP headers.** - **Implement guardrails and safety UX patterns (content moderation messages, blocked actions, restricted inputs, fallback UIs) aligned with enterprise AI governance.** - **Perform comprehensive testing using Jest, React Testing Library for end-to-end flows, including streaming interactions and agent workflows.**