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.