AI Engineer – Azure AI Specialist (Consultant / Part-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.