TECHNICAL LEAD - Gen AI

Min 8 + Years--FullStack + Ai developer

 

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.   Keywords: (Enterprise backend (Java, SpringBoot), AI agent orchestration (LangChain, LangGraph), LLM systems, RAG, Vector Databases (PgVector), Python, FAST API, Azure)
Key Responsibilities Frontend (React)  

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 timelinesusing 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.

Integrate front-end apps with 3rd part services like Azure services, Azure App Service, Azure AD authentication flows etc.