Job Summary

- Design, build, and deploy GenAI solutions on Azure using Azure OpenAI, AI Foundry, and Python, following enterprise security and governance standards.

- Implement Retrieval-Augmented Generation (RAG) architectures: document ingestion, chunking, embeddings, vector indexing, retrieval strategies, and response generation with citations/grounding.

- Design and optimize Azure AI Search (keyword + vector + hybrid) indices, skillsets, and enrichment pipelines to improve retrieval quality and latency.

- Develop prompt engineering patterns, tool/function calling, and agentic orchestration workflows using Azure cloud-native frameworks such as Semantic Kernel, Azure AI Foundry (Agents/Agent Service) and Prompt Flow, with strong evaluation, safety, and fallback strategies.

- Integrate AI solutions with Azure services such as Azure Functions, App Service/Container Apps, AKS, API Management, Event Grid/Service Bus, Storage, Key Vault, and Azure Monitor.

- Implement LLMOps/MLOps practices: CI/CD for prompts/models, environment promotion, model/prompt versioning, automated evaluation, and monitoring for drift, cost, and quality.

- Build data pipelines for unstructured and structured data, including extraction, normalization, metadata management, and access controls for enterprise knowledge bases.

- Apply Responsible AI practices: content filtering, prompt injection defenses, PII handling, data residency considerations, and human-in-the-loop review where needed.

- Estimate and manage Azure AI solution costs (token usage, search queries, storage, compute) and optimize performance through caching, batching, and retrieval tuning.

- Collaborate with stakeholders to translate business problems into AI use cases, define success metrics, and deliver production-ready solutions.

- Perform code reviews and establish engineering standards for Python services, API design, testing, security, and reliability for AI applications.





- Maintain clear documentation for architecture, prompts, evaluation results, data sources, and operational runbooks; support incident triage and continuous improvements.

Key Responsibilities

- 10+ years of overall experience with strong recent hands-on delivery of AI/ML and GenAI solutions on Microsoft Azure.

- Strong expertise in Retrieval-Augmented Generation (RAG) patterns, embeddings, vector databases/indexes, and evaluation of retrieval + generation quality.

- Hands-on with Azure AI Search including vector/hybrid search, semantic ranking, index design, skillsets/enrichment, and performance tuning.

- Hands-on with Azure OpenAI (chat/completions, embeddings, function calling/tools) and AI Foundry for building, evaluating, and operationalizing GenAI apps.

- Hands-on experience with Azure cloud-native agentic frameworks and tooling such as Semantic Kernel, Azure AI Foundry (Agents/Agent Service) and Prompt Flow to build orchestrated, tool-using AI applications.

- Understanding of MCP (Model Context Protocol) and how it applies to integrating tools/connectors, managing context, and enabling interoperable agent workflows in enterprise GenAI solutions.

- Strong Python engineering skills (FastAPI/Flask preferred), with experience building REST APIs and integrating with enterprise systems.

- Experience with Azure AI services and data components such as Azure AI Services (Cognitive Services), Azure Machine Learning, Data Factory/Synapse, Blob/ADLS, and Cosmos DB (as applicable).





- Experience implementing CI/CD and LLMOps/MLOps using Azure DevOps or GitHub Actions, with automated tests, evaluations, and deployment pipelines.

- Good knowledge of Azure security and governance: Entra ID, Managed Identities, Key Vault, network isolation/private endpoints, RBAC, logging/monitoring, and compliance considerations.

- Solid troubleshooting and analytical skills; ability to diagnose retrieval quality issues, hallucinations, latency bottlenecks, and production incidents.

- Excellent communication and stakeholder management; ability to explain AI concepts, risks, and trade-offs to technical and non-technical audiences.

Skill Requirements

- Experienced Azure AI Engineer with 10+ years of experience designing and delivering AI/ML and GenAI solutions on Microsoft Azure. Strong hands-on expertise with Retrieval-Augmented Generation (RAG), Azure AI Search, Azure OpenAI, AI Foundry, and Python to build secure, scalable, production-grade AI applications. Collaborates closely with architecture, development, data, and operations teams to implement end-to-end AI solution lifecycles—from experimentation and prompt engineering to deployment, monitoring, and continuous improvement—following Agile/Scrum delivery practices.

Must Have Skill

Azure AI Search

Good to Have Skill

Other Requirements

Experienced Azure AI Engineer with 10+ years of experience designing and delivering AI/ML and GenAI solutions on Microsoft Azure. Strong hands-on expertise with Retrieval-Augmented Generation (RAG), Azure AI Search, Azure OpenAI, AI Foundry, and Python to build secure, scalable, production-grade AI applications. Collaborates closely with architecture, development, data, and operations teams to implement end-to-end AI solution lifecycles—from experimentation and prompt engineering to deployment, monitoring, and continuous improvement—following Agile/Scrum delivery practices.

📌 Sr Subject Matter Expert (Support&Ops) (India)
🏢 HCLTech
📍 India