Agentic AI

Job Description

Required Information Details

1 Role -Agentic AI Engineer / Lead

2 Desired Experience Range -5-10 years of overall experience, including 3+ years delivering Generative AI and Agentic AI solutions.

3 Location of Requirement - Hyderabad/Chennai/Bengaluru

PRIMARY SKILLS: Python, LangChain, LangGraph , Agentic AI

Desired Competencies (Technical/Behavioral Competency)

Must-Have**

Primary Mandatory Skills

Python

• Strong hands-on Python development for AI applications, including object-oriented design, asynchronous programming, API integration, testing and production-quality coding.
• Experience with Python frameworks and libraries used for agent development, orchestration, data handling and service integration.

LangChain

• Strong hands-on development using LangChain for model integration, tools, prompts, retrieval, structured outputs and agent composition.
• Experience building reusable enterprise AI components and integrating APIs, data sources and vector stores.

LangGraph

• Strong hands-on development of stateful, multi-step and multi-agent workflows using graphs, checkpoints, persistence, branching, retries and human approval nodes.
• Ability to design resilient orchestration for long-running workflows, error recovery and controlled execution.

Agentic AI

• Hands-on design and implementation of production-grade autonomous agents with planning, reasoning, memory, tool use, guardrails, feedback loops and human-in-the-loop controls.
• Ability to translate business workflows into reliable agentic architectures and measurable outcomes.

Candidates must demonstrate practical project experience across all four primary skills: Python,LangChain, LangGraph ,Agentic AI.

Nice To have

MCP (Model Context Protocol)

• Hands-on implementation and integration of MCP servers and clients to expose and consume tools, prompts, resources and enterprise context.
• Experience with MCP transport, authentication, authorization, schema design, security controls and tool governance.

A2A (Agent-to-Agent Protocol)

• Hands-on implementation of interoperable multi-agent communication, including agent discovery, task delegation, message exchange, coordination and result sharing.
• Experience integrating specialized agents across frameworks or services using secure, standards-based A2A patterns.

Agent UI (Agent-User Interaction Protocol)

• Design intuitive agent-user interaction experiences.
• Implement conversational interfaces, human-in-the-loop workflows, and feedback mechanisms.
• Develop adaptive interaction protocols for enterprise AI applications.
• Ensure transparency, explainability, and governance within AI interactions.