Position Profile: Full-Stack Developer – Investment Analytics Platform We’re building a next-generation investment analysis system — where historical data, sector intelligence, macro signals, and holding trends can be queried using natural language and interpreted through charts, models, and narratives. We are looking for a hands-on developer with experience in: • Structuring scalable databases • Integrating LLMs (like GPT-4) into analytical workflows • Building interactive front ends • Supporting data science-driven discovery and analysis You will work closely with the founders and be part of a small, focused team creating a high-impact internal tool Company : ITUS Capital– an Asset Management firm. (https://ituscapital.com/about-us/ ) Role : Full-Stack Developer – Investment Analytics Platform Reporting to : The Founding Team Location : Chennai Skillset · Experience with Python/Streamlit, PostgreSQL and/or vector databases · 4+ years of experience applying machine‑learning or statistical models (financial markets experience is a plus) · Comfort working with time‑series and cross‑sectional data · Proven ability to design experiments and iterate rapidly on model ideas · Clear communicator who can translate technical insights for non‑technical stakeholders · Exposure to the full ML workflow: feature engineering, validation, tuning, and deployment Relevant work experience in yrs · Candidates should have 4–8 years of hands-on development experience, with a demonstrated track record of: Designing and maintaining data pipelines and time-series databases, preferably in financial or economic domains · Building full-stack analytical applications using Python (FastAPI, Pandas) and React or Streamlit · Working with structured (SQL) and unstructured data (PDFs, text, CSV) for research and analytics · Integrating LLM APIs (e.g., OpenAI, Cohere, Claude) to extract in-sights, generate summaries, or translate queries into code · Implementing retrieval-augmented generation (RAG) flows using vector databases Background industry preference: Finance, Research, Data Science, Technology Responsibilities · Build and maintain a unified data warehouse (20+ years of financial & macro data) · Create data pipelines to ingest returns, fundamentals, macro, and holdings · Develop APIs that connect LLMs to structured data (RAG, SQL → Natural Language) · Integrate ML models (XGBoost, clustering, time-series analytics) into backend · Design front-end interface for interactive querying, pattern discovery, and visualizations · Implement pattern logging, summarization, and export modules · Optimize for performance, security, and modularity
