Azure AI/ML Engineer

Interview Mode : Weekend Walkin (Face to Face )

Interview Date : 19 th Sep -26(Saturday )

Note : Only apply Chennai Location candidates

Role : Azure AI/ML Engineer

Experience : 5 to 10 years

Location : Chennai

The Role :

In this role, the candidate will join a cutting‑edge engineering team focused on designing and implementing enterprise‑grade AI solutions using Azure AI Document Intelligence, Azure OpenAI, and Azure AI Foundry. The position offers the opportunity to work with best‑in‑class Microsoft AI technologies to build intelligent automation capabilities, accelerate business processes, and elevate digital experiences across the organization.

The Azure AI Engineer will be responsible for developing secure, scalable, and high‑performing AI solutions, including document intelligence workflows, generative AI applications, and retrieval‑augmented systems. This includes working on model orchestration, prompt engineering, embeddings, chat completions, LLM API integrations, document extraction pipelines, and system integrations with enterprise platforms such as CRM, ERP, and workflow automation tools.

The role requires close collaboration with architects, product owners, data engineers, and cross‑functional teams to translate business requirements into technical AI solutions. Beyond development, the engineer will continuously monitor, evaluate, and optimize AI workloads, ensuring accuracy, compliance, and alignment with organizational standards.

This is a hands‑on engineering role requiring strong problem‑solving abilities, in‑depth understanding of Azure AI capabilities, and a passion for driving innovation with LLMs and intelligent document processing.

Your Responsibilities

• Design, develop, and deploy AI solutions using Azure AI Document Intelligence, Azure OpenAI, and Azure AI Foundry.
• Build and operationalize LLM‑based applications, including prompt engineering, context engineering, embeddings, chat completions, and grounding strategies.
• Develop intelligent document processing pipelines using Azure AI Doc Intelligence for document classification, extraction, OCR, and automated workflows.
• Implement RAG architectures using Azure Cognitive Search or vector databases for retrieval grounding, chunking, ranking, and citation‑based responses.
• Integrate LLM and AI capabilities with enterprise systems using REST APIs, Azure Functions, APIM, Logic Apps, or Power Platform.
• Build cloud-native AI services using Azure Functions, Azure Resource Groups, App Services, Storage Accounts, App Insights, and Key Vault.
• Develop and maintain clean, secure, and well-tested Python or C# code following engineering best practices.