Full Stack Developer Agentic Systems (Tamil Nadu)

We are looking for a Full Stack Developer: Agentic Systems to build the product layer for AI-native workflows. This role focuses on turning LLMs, agents, memory, and external tools into reliable, production-grade user experiences. You will design and ship systems where agents can plan, execute multi-step tasks, recover from failures, maintain context, and deliver consistent value across sessions. Responsibilities - Build end-to-end product features across frontend, backend, and AI integrations - Design agent workflows that support planning, tool use, failure handling, and recovery - Integrate LLMs, memory, RAG systems, and external tools into production systems - Build real-time AI interactions using streaming, partial results, and low-latency responses - Improve reliability, observability, fallback logic, and production behavior of AI workflows - Collaborate with ML, backend, product, and design teams to ship features from concept to production - Iterate on AI workflows based on user behavior, evaluation results, and observed failure modes - Establish reusable patterns for building scalable agentic systems Requirements - Strong full stack engineering experience across frontend and backend development - Solid understanding of system design, APIs, and production-grade architecture - Experience building with LLMs, RAG systems, agents, or AI-powered applications - Ability to work through ambiguity and make pragmatic engineering decisions - Solid ownership mindset with experience taking features from idea to production Tech Stack & Skills Core Engineering - Next.js, Node.js, Python - SQL and NoSQL databases - API design and backend architecture - Docker AI & Agentic Systems - LLM integration using OpenAI, Anthropic, or open-source models - Agent workflows, tool use, memory, or RAG-based systems - Streaming responses and real-time AI interaction patterns - Agent frameworks: LangChain, LlamaIndex, CrewAI, AutoGen, or similar - Vector databases: Pinecone, Weaviate, Qdrant, Milvus, or pgvector Production & Reliability - Observability, reliability, fallback handling, and debugging in production - Experience with evaluation frameworks for LLM or agent performance - Experience with workflow orchestration systems Nice to Have - Familiarity with prompt engineering, retrieval strategies, and context management - Experience building AI products beyond chat-based interfaces We are looking for a Full Stack Developer: Agentic Systems to build the product layer for AI-native workflows. This role focuses on turning LLMs, agents, memory, and external tools into reliable, production-grade user experiences. You will design and ship systems where agents can plan, execute multi-step tasks, recover from failures, maintain context, and deliver consistent value across sessions. Responsibilities - Build end-to-end product features across frontend, backend, and AI integrations - Design agent workflows that support planning, tool use, failure handling, and recovery - Integrate LLMs, memory, RAG systems, and external tools into production systems - Build real-time AI interactions using streaming, partial results, and low-latency responses - Improve reliability, observability, fallback logic, and production behavior of AI workflows - Collaborate with ML, backend, product, and design teams to ship features from concept to production - Iterate on AI workflows based on user behavior, evaluation results, and observed failure modes - Establish reusable patterns for building scalable agentic systems Requirements - Strong full stack engineering experience across frontend and backend development - Solid understanding of system design, APIs, and production-grade architecture - Experience building with LLMs, RAG systems, agents, or AI-powered applications - Ability to work through ambiguity and make pragmatic engineering decisions - Solid ownership mindset with experience taking features from idea to production Tech Stack & Skills Core Engineering - Next.js, Node.js, Python - SQL and NoSQL databases - API design and backend architecture - Docker AI & Agentic Systems - LLM integration using OpenAI, Anthropic, or open-source models - Agent workflows, tool use, memory, or RAG-based systems - Streaming responses and real-time AI interaction patterns - Agent frameworks: LangChain, LlamaIndex, CrewAI, AutoGen, or similar - Vector databases: Pinecone, Weaviate, Qdrant, Milvus, or pgvector Production & Reliability - Observability, reliability, fallback handling, and debugging in production - Experience with evaluation frameworks for LLM or agent performance - Experience with workflow orchestration systems Nice to Have - Familiarity with prompt engineering, retrieval strategies, and context management - Experience building AI products beyond chat-based