Azure AI/ML Engineer

Interview Mode : Weekend Walkin drive (Face to Face )

Interview Date : 19-Sep-26(Saturday )

Note : Only candidates currently located in should a Chennai apply.

Role : Azure AI/ML Engineer

Experience : 5 to 10 Years

Location : Chennai

Must-Have

• 5–8 years of experience in software engineering or AI/ML development, with at least 4+ years working with Azure AI services.
• Strong hands-on experience with Python for backend development, AI pipelines, and LLM integrations.
• Strong hands-on experience with Azure AI Document Intelligence including extraction, forms, OCR, and classification.
• Deep understanding of Azure OpenAI, including GPT models, embeddings, chat completions, prompt engineering, and content safety.
• Practical experience using Azure AI Foundry for building, testing, and operationalizing AI apps.
• Experience building AI solutions in cloud-native Azure environments including:
• Azure Functions
• Azure API Management (APIM)
• Azure Resource Groups
• Azure Storage Accounts
• App Insights
• Azure App Service
• Azure Key Vault
• Hands-on experience making calls to LLM APIs (chat completions, embeddings, model inference endpoints) and integrating them into applications.
• Experience architecting and implementing RAG systems using Azure Cognitive Search or vector index stores.
• Strong programming experience in Python or C# (Node.js acceptable) for backend microservices and integrations.
• Practical experience with Azure services including Logic Apps, Event Grid, Storage, Functions, and automated workflows.
• Solid knowledge of Responsible AI, privacy, compliance, and secure deployment models.
• Strong debugging and optimization skills for AI workloads (cost, latency, throughput).
• Hands-on experience with Azure DevOps, Git repositories, CI/CD pipelines, IaC, and deployment automation.
• Strong analytical, communication, and stakeholder management skills.

Desirable Skills / Knowledge / Experience

• Experience using vector databases such as Azure Cosmos DB with vector indexing, Redis Enterprise, or Pinecone.
• Familiarity with multi-agent AI architectures, orchestration frameworks, and agentic workflows.
• Exposure to Power Platform AI Builder and low-code AI integrations.
• Understanding of NLP techniques including entity extraction, embeddings, semantic search, text analytics, and conversation design.
• Experience with frameworks like LangChain, Semantic Kernel, or LlamaIndex.