Agentic AI Developer

JOB DESCRIPTION

Agentic AI Developer

Enterprise GenAI | LLM Applications | RAG | Agent Orchestration | Cloud Deployment

Role

Agentic AI Developer

Experience

4-10 years

Employment Type

Full-time

Domain Preference

Manufacturing, Supply Chain, Finance, Customer Operations, Enterprise Support

Primary Skill Set

Python, LLMs, RAG, Agentic AI frameworks, API integration, cloud deployment

Role Summary

We are seeking an experienced Agentic AI Developer to design, develop, and deploy intelligent autonomous AI systems capable of planning, reasoning, tool orchestration, memory management, and task execution. The role requires strong expertise in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), multi-agent frameworks, workflow orchestration, cloud-native deployment, and enterprise integration.

The candidate will build next-generation AI solutions that combine LLM reasoning with enterprise applications, APIs, business rules, and human-in-the-loop controls to automate complex business processes across Manufacturing, Supply Chain, Finance, Customer Operations, and Enterprise Support functions.

Key Responsibilities

Agentic AI Design & Development

• Design and implement autonomous AI agents capable of planning, reasoning, and executing multi-step workflows.
• Build single-agent and multi-agent architectures using frameworks such as LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel, and OpenAI Agents.
• Develop human-in-the-loop workflows with approval mechanisms and governance controls.
• Implement memory, tool selection, planning loops, and feedback mechanisms for agent optimization.

Generative AI & RAG

• Build and optimize enterprise-grade RAG solutions.
• Integrate vector databases such as Pinecone, FAISS, ChromaDB, Azure AI Search, or Weaviate.
• Design prompt engineering, evaluation, grounding, and hallucination mitigation frameworks.
• Fine-tune LLM workflows and optimize token usage, latency, and cost.

Enterprise Integration

• Integrate AI agents with ERP systems, manufacturing systems, CRM platforms, service management platforms, databases, and APIs.
• Develop reusable toolkits and connectors for enterprise workflows.

Cloud & Platform Engineering

• Deploy AI solutions using Azure, AWS, or GCP.
• Build scalable APIs using FastAPI, Flask, or microservices architecture.
• Containerize and deploy solutions using Docker and Kubernetes.
• Implement CI/CD pipelines and MLOps practices.

Governance & Reliability

• Implement security, guardrails, compliance, auditability, and policy enforcement.
• Monitor agent performance, observability metrics, and operational efficiency.
• Build evaluation frameworks for reliability, safety, and business outcome measurement.

Required Technical Skills

Skill Area

Expected Capability

Programming

Python, SQL, REST APIs, Data Structures & Algorithms

AI & Machine Learning

Machine Learning, Deep Learning, NLP, LLM Architectures, Prompt Engineering, Embeddings & Vector Search

Agentic AI

LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel, MCP, Multi-Agent Systems, Tool Calling & Function Calling

RAG & Search

RAG Architecture, Knowledge Retrieval, Vector Databases, Hybrid Search, Knowledge Graphs

Cloud & DevOps

Azure OpenAI / Azure AI Foundry, AWS Bedrock, Google Vertex AI, Docker, Kubernetes, GitHub Actions, Azure DevOps

Observability & Evaluation

LangSmith, PromptFlow, OpenTelemetry, AI Evaluation Frameworks

Note : please dont apply previous and current emp of TCS