Sr Subject Matter Expert (Support&Ops)

• *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.
• Strong 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**
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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**
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Azure AI Search

• *Good to Have Skill**
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• *Other Requirements**
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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.