Agentic AI Engineer

Job Summary

We are looking for an experienced Agentic AI Engineer to design, develop, and deploy autonomous AI agents capable of performing complex business tasks with minimal human intervention. The ideal candidate will have strong expertise in Generative AI, Large Language Models (LLMs), agent frameworks, orchestration platforms, retrieval systems, and cloud-native architectures.

The role involves building intelligent AI solutions leveraging multi-agent systems, reasoning frameworks, tool integrations, workflow automation, and enterprise AI governance to drive business transformation.

Key Responsibilities

• Design and develop autonomous AI agents using modern agentic AI frameworks.
• Build multi-agent systems capable of planning, reasoning, task execution, and self-correction.
• Develop Retrieval-Augmented Generation (RAG) solutions using vector databases and knowledge graphs.
• Integrate AI agents with enterprise applications, APIs, databases, and business workflows.
• Implement prompt engineering, agent orchestration, memory management, and tool usage strategies.
• Fine-tune and optimize LLMs for specific enterprise use cases.
• Build scalable and secure AI services on cloud platforms.
• Monitor AI agent performance, hallucination rates, latency, and operational effectiveness.
• Ensure responsible AI practices, compliance, security, and governance standards.
• Collaborate with product managers, architects, data engineers, and business stakeholders to deliver AI-driven solutions.
• Establish observability, evaluation, and testing frameworks for AI agents in production.

Required Skills

Generative AI & LLMs

• Strong experience with GPT, Claude, Gemini, Llama, Mistral, or similar LLMs.
• Deep understanding of prompt engineering, chain-of-thought reasoning, and AI workflows.
• Experience with fine-tuning, embedding models, and model evaluation techniques.

Agentic AI Frameworks

• LangChain
• LangGraph
• CrewAI
• AutoGen
• Semantic Kernel
• LlamaIndex
• OpenAI Agents SDK

RAG & Knowledge Systems

• Vector databases:
  • Pinecone
  • Weaviate
  • ChromaDB
  • FAISS
  • Milvus
• Knowledge Graphs
• Document processing pipelines
• Semantic search architectures

Programming

• Python (Mandatory)
• FastAPI
• REST APIs
• Microservices Architecture
• SQL and NoSQL databases

Cloud Platforms

• Azure AI Services
• Azure OpenAI
• GCP Vertex AI
• AWS Bedrock
• Kubernetes
• Docker

MLOps & AI Operations

• MLflow
• Prompt Flow
• LangSmith
• Arize AI
• Weights & Biases
• Model monitoring and evaluation frameworks

Preferred Qualifications

• Experience building enterprise-grade AI copilots and intelligent assistants.
• Hands-on experience with multi-agent orchestration and workflow automation.
• Knowledge of reinforcement learning and AI planning techniques.
• Experience integrating AI solutions with ERP, CRM, Retail, Supply Chain, or Customer Service platforms.
• Familiarity with Responsible AI, AI Security, and Governance frameworks.
• Understanding of MCP (Model Context Protocol) and AI agent interoperability standards.

Educational Qualification

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or related discipline.

Nice to Have

• Experience with Retail domain applications.
• Knowledge of graph databases such as Neo4j.
• Experience building AI-powered automation solutions.
• Contributions to Generative AI open-source projects.
• AI/Cloud certifications from Microsoft Azure, AWS, or Google Cloud.

Key Competencies

• Agent Design & Orchestration
• AI Reasoning & Planning
• Prompt Engineering
• Multi-Agent Systems
• RAG Architecture
• LLM Optimization
• Cloud Architecture
• Solution Design
• Stakeholder Management
• Problem Solving & Innovation