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.