Python Agentic AI

Key Responsibilities

• Design, develop, and deploy AI-powered applications using Python and Agentic AI frameworks.
• Build and manage single-agent and multi-agent systems for complex business workflows.
• Implement Model Context Protocol (MCP) for seamless tool and context integration.
• Develop Agent-to-Agent (A2A) communication mechanisms for collaborative AI agents.
• Integrate LLMs with external tools, APIs, databases, and enterprise systems.
• Design orchestration workflows for autonomous AI agents.
• Optimize AI agent performance, scalability, and reliability.
• Collaborate with AI engineers, data scientists, and product teams to deliver enterprise AI solutions.
• Troubleshoot, debug, and enhance AI applications while following best coding practices.

Must-Have Skills

• 5–9 years of software development experience with strong expertise in Python.
• Hands-on experience with Agentic AI development.
• Strong knowledge of Multi-Agent Architecture and autonomous AI systems.
• Experience with Model Context Protocol (MCP).
• Experience implementing Agent-to-Agent (A2A) communication.
• Experience integrating LLMs (OpenAI, Anthropic, Gemini, Llama, etc.) into enterprise applications.
• Knowledge of REST APIs, JSON, asynchronous programming, and microservices.
• Familiarity with vector databases, RAG, prompt engineering, and AI orchestration frameworks.
• Experience with Git, Docker, and cloud platforms (AWS, Azure, or GCP).

Good-to-Have Skills

• Experience with LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel, or similar Agentic AI frameworks.
• Knowledge of Kubernetes, CI/CD pipelines, and MLOps practices.
• Experience with databases such as PostgreSQL, MongoDB, or Redis.
• Exposure to AI monitoring, evaluation, and observability tools.