Posted:1 day ago|
Platform:
On-site
Full Time
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
·        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
·        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
·        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
Itus Capital Advisors Pvt Ltd
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