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

  .

Technical Project Lead

• 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.