Full Stack Developer

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

Role

Reporting to

Location

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:

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