Senior Agentic Ai Engineer

About the Role

Senior Agentic AI Engineer to design, build, and operate production-grade agentic AI, Decision Intelligence, and Retrieval-Augmented Generation (RAG) solutions on Microsoft Azure for enterprise supply chain systems. The role focuses on architecting scalable, secure, explainable AI agents and platforms, integrating them with enterprise data and systems, and establishing governance, observability, and evaluation frameworks.

Explicitly centered on agentic/LLM-driven engineering—building RAG, multi-agent systems, and AI tooling on Azure for rapid, intelligent automation.

Location: Hyderabad, Chennai, Bengaluru

Type: Full Time, Permanent

Posted: 2 days ago

Category: AI/ML & Data

Vibe Markers: 🔥 Hot

Experience Level: Senior

Job Description

Role

We are seeking a hands-on Senior Agentic AI Engineer to design, build, and operate production-grade agentic AI, Decision Intelligence, and Retrieval-Augmented Generation (RAG) solutions on Microsoft Azure for enterprise supply chain systems. The engineer will create reusable, scalable, secure, and explainable AI capabilities that integrate with enterprise data and applications.

Key Responsibilities

• Design and implement single-agent and multi-agent systems capable of reasoning, planning, tool usage, and workflow execution.
• Architect generative AI solutions using Azure AI Foundry, Azure OpenAI, Azure AI Search, and other Azure services.
• Build and govern RAG architectures using structured and unstructured enterprise data (embeddings, vector/hybrid search, chunking, metadata filtering, reranking, grounding, citations).
• Implement secure integrations between agents and enterprise systems, including NL→SQL capabilities and authorization boundaries.
• Define semantic-layer strategies to map enterprise data models, business metrics, terminology, and relationships for agent use.
• Architect decision intelligence capabilities that combine AI, analytics, business context, and workflows; create frameworks for impact analysis, prioritization, and traceability.
• Define and automate evaluation methodologies (golden datasets, LLM-based evaluation, regression) and integrate them into CI/CD pipelines.
• Implement Responsible AI controls and governance: content safety, PII protection, grounding checks, human-in-the-loop escalation, and compliance standards.
• Implement observability and monitoring for quality, performance, reliability, traces, tool calls, latency, token usage, and cost.
• Optimize for cost and latency via model selection, prompt/context engineering, caching, and workload sizing.
• Drive architectural standards, reference architectures, and reusable frameworks; mentor engineers and partner with stakeholders and data engineering teams.

Requirements

• Bachelor’s degree in Computer Science or related field (Master’s preferred).
• ~8+ years of relevant software engineering experience and 3+ years of hands-on experience with generative AI, ML, intelligent automation, or LLM-based applications, including recent agentic AI work.
• Strong programming proficiency in Python; working knowledge of TypeScript/Node.js or comparable languages.
• Experience designing and deploying production-grade cloud-native applications with strong software engineering, DevOps, and MLOps/LLMOps practices.
• Hands-on experience building Generative AI and RAG solutions with Azure AI Foundry, Azure OpenAI, Azure AI Search, LLM APIs, embeddings, vector/hybrid search, grounding, and citations.
• Familiarity with agentic AI frameworks (Microsoft Agent Framework, Semantic Kernel, LangGraph, AutoGen) including tool calling, MCP-based integrations, and HITL controls.
• Experience with NL→SQL solutions, schema/metadata modeling, query generation/validation, and grounding answers in retrieved data.
• Experience designing decision intelligence systems combining AI, analytics, business context, and operational workflows.
• Experience with evaluation and improvement of agent quality: prompt engineering, test datasets, LLM-based evaluation, safety checks, and production feedback loops.
• Knowledge of CI/CD, Docker, Kubernetes, observability, and production operations for AI applications.
• Working knowledge of Azure platform services: AKS or Azure Container Apps, Azure Functions, API Management, Entra ID, Key Vault, and Azure SQL or Cosmos DB.
• Understanding of RESTful APIs, asynchronous patterns, secure integrations, relational databases, SQL/NoSQL, and ETL/data pipelines.

Preferred / Nice-to-have

• Experience with Microsoft Fabric, Azure Databricks, or Azure Data Factory for AI-ready pipelines.
• Exposure to Copilot Studio, Power Platform, or Teams-based agent experiences.
• Experience with model fine-tuning (LoRA/QLoRA), prompt caching, and token/cost optimization at scale.
• Supply chain domain knowledge (procurement, logistics, ERP systems like Oracle EBS or SAP).
• Front-end experience with React and TypeScript for agent-facing UIs.
• Microsoft certifications (e.g., Azure AI Engineer, Azure Solutions Architect).

Tech Stack

Azure AI Foundry, Azure OpenAI, Azure AI Search, Azure Functions, AKS, Azure Container Apps, API Management, Entra ID, Key Vault, Azure SQL, Cosmos DB, Azure Databricks, Azure Data Factory, Microsoft Fabric, Copilot Studio, Power Platform, Teams, Microsoft Agent Framework, Semantic Kernel, LangGraph, AutoGen, Python, TypeScript, Node.js, React, Docker, Kubernetes, LLM APIs, embeddings, vector search, hybrid search, REST APIs, MCP, Natural Language to SQL, SQL, NoSQL, LoRA, QLoRA

Skills

System Design, Software Architecture, Cloud Architecture, MLOps/LLMOps, DevOps, CI/CD, Observability & Monitoring, Prompt Engineering, Retrieval-Augmented Generation (RAG) design, Decision Intelligence, Security & Governance (Responsible AI), Semantic/Metadata Modeling, Natural Language to SQL / Conversational Analytics, Evaluation & Testing Methodologies, Mentoring, Collaboration, Problem Solving, Communication, Performance & Cost Optimization