Java Fullstack Agentic AI Programmer (Bengaluru)

Job Title: Java Full Stack Lead (Java, React, and Agentic AI) Experience: 8 to 12 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: - Design and develop Agentic AI systems capable of reasoning, planning, and executing complex workflows using Large Language Models. - Build AI-powered services using LLM APIs such as OpenAI, Azure OpenAI Service, or other foundation model providers. - Develop and orchestrate AI agents using frameworks such as LangChain, LangGraph, and LlamaIndex. - Design and implement multi-agent systems, including agent collaboration, task decomposition, and tool usage. - Build Retrieval-Augmented Generation (RAG) pipelines integrating enterprise knowledge sources. - Integrate vector databases such as PgVector, Pinecone, Weaviate, or Milvus to enable semantic search and knowledge retrieval. - Build scalable backend services using Java (Spring Boot / Netflix DGS) for enterprise integrations and high-throughput APIs. - Write Python services using Object-Oriented design principles to support LLM orchestration, prompt engineering, and agent execution. - Develop AI microservices using FastAPI to expose agent capabilities and LLM-powered workflows. - Integrate AI agents with enterprise systems via REST APIs, event streams, and databases. - Design and implement tool integrations enabling AI agents to interact with internal services, APIs, and automation workflows. - Implement memory architectures for AI agents including short-term memory, long-term knowledge retrieval, and context management. - Design observability, monitoring, and evaluation frameworks to measure LLM performance, agent behaviour, hallucination rates, and task success. - Optimize prompt engineering, model selection, token usage, latency, and cost efficiency. - Build guardrails and safety mechanisms for reliable AI system behaviour. - 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. - Design and run evaluation pipelines and experimentation frameworks to continuously improve AI agent accuracy, reliability, and performance. - 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 agile 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. .