Happiest Minds Technologies - Technical Lead - Java/React.js

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.