Agentic AI Architecture & Development

Agentic AI Architecture & Development Location: Bangalore (Hybrid – 3 days WFO mandatory) Budget: Up to INR 30 LPA Employment Type: Full-Time About the Role We are seeking an experienced Agentic AI Architect & Developer to design and build production-grade multi-agent systems leveraging LangGraph, LangChain, CrewAI, and AutoGen. The ideal candidate will have strong expertise in Agentic AI, LLM integration, and advanced Python development with proven experience in enterprise-scale AI/ML environments. Key Responsibilities Architect and implement multi-agent orchestration patterns (planning, tool use, persistent state, memory, reflection). Develop and optimize RAG pipelines with embeddings, chunking, and vector database integration. Integrate and manage LLM/SLM services (OpenAI, Azure OpenAI, Anthropic, open-source models) with cost optimization. Build scalable backend services using Python (FastAPI), distributed task processing (Celery), and event-driven microservices. Implement caching, rate limiting, persistent agent state, and conversation memory strategies. Lead innovation initiatives, mentor engineers, and champion adoption of AI-powered development tools (Cursor AI, GitHub Copilot). Required Qualifications 6+ years of proven software engineering experience with significant hands-on AI/ML work in enterprise environments. Strong proficiency in Python with production AI applications. Hands-on experience with LangGraph or similar frameworks (LangChain, CrewAI, AutoGen). Expertise in LLM API integration and prompt engineering. Experience designing and implementing RAG systems with vector databases. Solid understanding of multi-agent system design and orchestration. Strong communication skills to explain complex AI concepts to diverse stakeholders. Preferred Qualifications Experience with vector databases (Qdrant, Pinecone, Weaviate, ChromaDB). Familiarity with Databricks Genie or AWS Bedrock AgentCore. Knowledge of model fine-tuning, quantization, and serving optimization. Experience with containerization (Docker, Kubernetes) and event-driven autoscaling (KEDA). Understanding of AI safety, responsible AI principles, and enterprise governance. Interview Process 2 rounds of interviews. General shift timing; no weekend/on-call support required. Candidates with ≤15 days notice period or serving notice preferred.