Project Description

We are seeking an experienced AI/ML engineer to develop an intelligent financial assistant capable of analyzing company financial documents and answering user queries in natural language. The system will use Retrieval-Augmented Generation (RAG) and a fine-tuned language model to deliver accurate, context-aware insights.

Core Features:

Answer financial queries (e.g., profit margins, risks, summaries)
Summarize financial reports
Provide context-aware, domain-specific responses

Technical Requirements:

Strong Python skills with frameworks such as FastAPI
Experience with LLMs (Hugging Face, OpenAI APIs)
Hands-on experience with RAG pipelines
Experience with vector databases (FAISS, ChromaDB, etc.)
Knowledge of embeddings and semantic search
Experience with LLM fine-tuning (LoRA/QLoRA preferred)

Requirements:

Proven experience building AI/ML or NLP-based systems
Ability to design scalable and efficient architectures
Strong problem-solving and analytical skills

Project Goal:
Build a reliable AI assistant that can interpret financial data and provide clear, actionable insights through natural language interaction.