Job Description :

Key Responsibilities :

- Design and build Agentic AI architectures capable of autonomous task execution, reasoning, and planning.

- Develop and optimize LLM-driven multi-agent systems that coordinate, collaborate, and adapt dynamically.

- Implement retrieval-augmented generation (RAG) and memory-based reasoning pipelines for intelligent context use.

- Conduct prompt engineering, few-shot learning, and reinforcement learning to fine-tune agent behavior.

- Build and manage knowledge graphs, embedding databases, and semantic retrieval layers to power reasoning.

- Architect data ingestion, context extraction, and interaction layers that enable AI agents to interface with real-world APIs, databases, and systems.

- Lead experimentation, evaluation, and optimization cycles to improve agent accuracy, autonomy, and decision quality.

- Collaborate with AI engineers and product teams to integrate agents into scalable production systems.

Technical Skills :

- Core AI & ML : Python, PyTorch, TensorFlow, JAX, transformers, LLM tuning, prompt optimization

- Agentic Frameworks : LangChain, LlamaIndex, CrewAI, multi-agent orchestration

- Data & Knowledge Systems : FAISS, Pinecone, Milvus, Neo4j, ArangoDB

- Infrastructure : Docker, Kubernetes, AWS, GCP, Azure

- Advanced Capabilities : Self-improving agents, meta-learning, AI alignment, explainability