Agentic AI Developer - LLM/RAG

What You Will Do :

• Design, develop, and implement production-grade agentic AI solutions using Large Language Models (LLMs) and reasoning engines.

• Build, optimize, and maintain multi-agent systems capable of autonomous task execution and collaboration.

• Integrate external tools, REST APIs, enterprise applications, and knowledge bases to enhance AI agent capabilities.

• Develop Retrieval-Augmented Generation (RAG) pipelines using vector databases and enterprise data sources.

• Design intelligent workflows incorporating memory, planning, reasoning, tool usage, and self-correction mechanisms.

• Optimize AI agents for performance, scalability, reliability, cost efficiency, and safety.

• Build and maintain backend services and APIs using Python frameworks such as FastAPI or Flask.

• Test, evaluate, debug, and continuously improve agent workflows and AI model performance.

• Collaborate with cross-functional teams to integrate Agentic AI solutions into enterprise products and business workflows.

• Contribute to code reviews, technical documentation, and engineering best practices.

• Stay updated with the latest advancements in Generative AI, Agentic AI, and emerging AI frameworks.

Required Skills :

• Strong programming skills in Python.

• Hands-on experience with AI/ML frameworks such as PyTorch, TensorFlow, or Scikit-learn.

• Strong understanding of Large Language Models (LLMs), prompt engineering, and agentic AI concepts.

• Experience with agent frameworks such as LangChain, LangGraph, AutoGen, CrewAI, or LlamaIndex.

• Experience building Retrieval-Augmented Generation (RAG) applications using vector databases such as Pinecone, Weaviate, ChromaDB, Milvus, or FAISS.

• Experience integrating REST APIs, external tools, and enterprise systems into AI workflows.

• Understanding of AI memory, planning, reasoning, tool calling, and workflow orchestration.

• Familiarity with responsible AI practices, guardrails, LLM evaluation, and AI safety principles.

• Experience using Git and modern software development practices.

• Strong analytical, debugging, and problem-solving skills.

Good to Have :

• Experience with MLOps tools such as MLflow or DVC.

• Familiarity with Docker, Kubernetes, and CI/CD pipelines.

• Knowledge of distributed systems and scalable backend architectures.