Agentic AI Engineer

Talentd; Chennai; On-site; INR 800,000 - 1,800,000; Full time

Job summary

TalentD in Chennai is seeking an Agentic AI Engineer to design, develop and deploy autonomous AI agents capable of reasoning and multi-step task execution. You will collaborate with data scientists and software teams to build scalable AI agent ecosystems.

You will prototype, evaluate and deploy reliable agents in production, ensuring security and scalability across platforms. Your work will focus on leveraging LLMs, RAG and agent orchestration tools to automate business processes and improve

Qualifications

• Experience designing autonomous AI agents with reasoning and decision-making.
• Experience with LLMs, RAG, and AI orchestration frameworks.
• Experience integrating AI agents with enterprise applications, databases, APIs and cloud services.

Responsibilities

• Design and develop autonomous AI agents for multi-step task execution.
• Build AI workflows using LLMs, Retrieval-Augmented Generation (RAG) and agent orchestration frameworks.
• Integrate AI agents with enterprise applications, databases, APIs and cloud services.
• Develop prompt engineering strategies and optimize agent performance.
• Implement memory management, reasoning, planning and decision-making capabilities within AI systems.

Job description

Job Summary

An Agentic AI Engineer designs, develops and deploys autonomous AI agents that can reason, plan, make decisions and carry out complex tasks with minimal human intervention. The role involves building AI-powered solutions using Large Language Models (LLMs), generative AI frameworks, orchestration tools and automation platforms to improve business processes and user experiences. The engineer works with data scientists, software developers and business stakeholders to build scalable and secure AI agent ecosystems.

Key Responsibilities

• Design and develop autonomous AI agents for multi-step task execution.
• Build AI workflows using LLMs, Retrieval-Augmented Generation (RAG) and agent orchestration frameworks.
• Integrate AI agents with enterprise applications, databases, APIs and cloud services.
• Develop prompt engineering strategies and optimize agent performance.
• Implement memory management, reasoning, planning and decision-making capabilities within AI systems.