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.

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