AI Engineer – Azure AI Specialist

6 days ago

New Delhi, India High Volt Analytics Full time

Company DescriptionHigh Volt Analytics assists medium to large organizations in becoming AI ready, starting with Finance & Accounts automation. We focus on automating financial processes to deliver real-time insights and error-free reports, integrating data into a secure AI-ready foundation, offering interactive AI-powered insights, and developing enterprise AI roadmaps. Our clients include CFOs, Controllers, FP&A leaders, and CEOs who aim to move beyond manual processes and harness AI-driven competitive advantages.Core Focus Areas:- Build and optimize RAG (Retrieval-Augmented Generation) systems on Azure. - Deploy, fine-tune, and manage LLMs within Azure environments (OpenAI, HuggingFace, or custom models). - Deep integration with Azure AI Search, Cognitive Services, and Azure Machine Learning pipelines. - Collaborate on vector store (Qdrant / Azure AI Search), embeddings, and prompt orchestration. - Translate finance data structures into meaningful, conversational AI outputs.Must-Have Skills:- Strong experience with Azure AI Studio / Foundry or Azure OpenAI Service. - Proven knowledge of deploying and scaling LLM-based applications. - Practical hands-on with vector databases, embedding workflows, and tools like LangChain or Semantic Kernel. - Understanding of Power BI semantic models, finance document structures, and business logic. - Familiarity with Azure Machine Learning, Azure Functions, and Key Vault for secure orchestration. - Python-based AI pipeline development (FastAPI experience is a plus).Good-to-Have Skills:- Familiarity with financial or BI reporting systems (Power BI, QuickBooks, accounting workflows). - Experience integrating search + chat UIs (Streamlit, Gradio, React, etc.). - Background in building AI copilots or enterprise-focused assistants.Why This Role Matters:While your Technical Project Lead will guide the overall engineering and system integration, this AI Engineer will:- Specialize in the AI layer, handling model workflows, retrieval systems, and Azure-based intelligence. - Bridge the gap between business data and conversational output, enabling smarter responses. - Accelerate integration with Azure-native AI tools, reducing friction and increasing speed-to-market.

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