Hiring For Agentic AI

Key Responsibilities

• Design and develop AI agents and multi-agent systems for enterprise use cases.
• Build agentic workflows using frameworks such as LangChain, LangGraph, AutoGen, CrewAI or similar technologies.
• Develop LLM-powered applications using OpenAI, Azure OpenAI, Gemini, Claude or equivalent models.
• Implement RAG pipelines using vector databases and enterprise knowledge sources.
• Integrate AI agents with APIs, databases, enterprise applications, search engines, and external tools.
• Develop agent capabilities including planning, reasoning, memory, tool calling, task decomposition, and decision-making.
• Build prompt engineering strategies and optimize LLM responses for accuracy and reliability.
• Work with Python to develop scalable AI services and backend applications.
• Implement evaluation, monitoring, guardrails, observability, and responsible AI practices.
• Fine-tune or customize models where required and optimize inference performance.
• Deploy AI solutions on AWS, Azure, or GCP using containers and CI/CD pipelines.
• Collaborate with Data Scientists, ML Engineers, Software Engineers, Architects, and business teams.
• Troubleshoot and improve the performance, scalability, security, and reliability of AI applications.

Required Skills

• Strong programming experience in Python.
• Hands-on experience with Generative AI and Large Language Models (LLMs).
• Strong understanding of Agentic AI concepts and autonomous AI workflows.
• Experience with LangChain / LangGraph / AutoGen / CrewAI or similar frameworks.
• Strong knowledge of RAG, embeddings, vector databases, semantic search, and knowledge retrieval.
• Experience with prompt engineering and LLM orchestration.
• Experience integrating REST APIs, tools, function calling, and enterprise systems.
• Knowledge of FastAPI, Flask, or similar Python frameworks.
• Experience with databases such as PostgreSQL, MongoDB, SQL Server or similar.
• Experience with vector databases such as Pinecone, FAISS, Chroma, Weaviate, Milvus or equivalent.
• Understanding of ML/AI lifecycle, model evaluation, monitoring, and deployment.
• Good understanding of cloud platforms such as AWS, Azure, or GCP.