AI Engineer – Azure AI Specialist

1 week ago

India High Volt Analytics Full time

Company Description High 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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