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.- Experience deploying AI applications on AWS, Microsoft Azure, or Google Cloud Platform.- Knowledge of Model Context Protocol (MCP).- Experience with AgentOps, monitoring, and observability tools.- Familiarity with fine-tuning techniques such as LoRA and QLoRA.- Experience building AI copilots, enterprise assistants, workflow automation, or document intelligence solutions.Qualifications : - Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Information Technology, Data Science, Software Engineering, or a related field.- Hands-on experience through internships, professional projects, research, hackathons, or open-source contributions in AI, LLMs, or Agentic AI.- Strong communication, collaboration, and problem-solving skills.- Passion for learning and building next-generation AI solutions.