Role Overview

We are looking for an AI Engineer – Generative AI & Agentic Systems with 3–8 years of experience in building and deploying enterprise-grade AI solutions using LLMs.

The ideal candidate should have strong hands-on experience in Generative AI, Agentic AI, RAG, Multi-Agent Systems, AI orchestration and Python . The role will involve developing AI assistants, enterprise copilots, autonomous workflows and scalable AI platforms.

Key Responsibilities

- Design, develop and deploy enterprise-grade applications powered by LLMs and foundation models.
- Build RAG solutions using vector databases, enterprise knowledge repositories and GraphRAG.
- Develop conversational AI assistants, enterprise copilots, chatbots and workflow automation solutions.
- Design and implement Multi-Agent AI systems for planning, reasoning, task decomposition and execution.
- Build AI orchestration frameworks integrating enterprise applications, APIs and external services.
- Implement agent memory, communication protocols and human-in-the-loop workflows.
- Develop scalable AI services, APIs and reusable AI platform components.
- Integrate AI solutions with enterprise platforms such as CRM, ERP, Supply Chain and Knowledge Management systems.
- Implement AI governance, monitoring, observability and evaluation frameworks.
- Evaluate AI applications for accuracy, relevance, groundedness, hallucination and safety.
- Optimize model inference, latency, scalability, token usage and infrastructure costs.
- Collaborate with Product, Engineering, Data Science and Business teams to deliver production-ready AI solutions.

Required Skills

- 3–8 years of experience in AI,



ML or Software Engineering with solid GenAI focus.
- Hands-on experience with LLMs and foundation models .
- Strong experience in RAG and vector databases .
- Experience building Conversational AI, AI Assistants and Enterprise Copilots .
- Hands-on experience with Multi-Agent AI systems and autonomous workflows .
- Strong proficiency in Python, FastAPI, REST APIs and Async Programming .
- Experience with AI orchestration frameworks such as LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel or Pydantic AI .
- Strong understanding of Prompt Engineering, Model Evaluation and Production Deployment .
- Experience building scalable AI APIs and enterprise-grade AI applications.

Technical Stack GenAI/LLMs: OpenAI, Claude, Gemini, Llama, Mistral, Qwen

Agentic AI: LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel, Pydantic AI

RAG: Vector Databases, Knowledge Graphs, GraphRAG

Programming: Python, FastAPI, REST APIs, Async Programming

Cloud/AI Platforms: Azure AI Foundry, Azure OpenAI, Vertex AI, AWS Bedrock, Databricks AI

Deployment: Docker, Kubernetes, GitHub Actions, CI/CD

Good to Have

- Experience building enterprise AI copilots and digital assistants.
- Production experience with Multi-Agent AI systems.
- Knowledge of AI governance, observability, monitoring and Responsible AI.
- Understanding of Model Context Protocol (MCP) .
- Domain experience in Retail, CPG, Banking, Telecom, Healthcare or Manufacturing.
- Familiarity with MLOps, model lifecycle management and cloud-native AI deployments.

Education Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science or a related field .

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