Company Vision

StockGro is a mobile-first cross-platform (Android & iOS, Mobile + Web App) Fintech product that’s empowering 25 million+ users to master the art of trading and investment in a risk-free and gamified manner. At StockGro - India’s First and Largest Social Investment Platform, users indulge in Social Investing and learn various trading strategies by interacting with leading fund managers, F&O traders, and algo traders.

About StockGro

Founded in January 2020 by former venture capitalist Ajay Lakhotia, we’re well-funded and just closed a massive Pre-Series A fundraise. We are backed by some of the respected investors - Roots Ventures, Velo Partners, Creed Capital, and the likes of Kunal Shah, Vivekananda Hallekere, Rahul Garg as Angels.

We have some brilliant minds with us, working on a mission to make 400 million Indian millennials investment-ready, with Senior Executives from Sequoia, Swiggy, Glance, Airtel, Uber, and institutions like ISB, NITs, and IIMs.

What you’ll work on:

• Designing and building GenAI / LLM-based systems for wealth advisory use cases (personalised insights, portfolio intelligence, conversational advisory) Or Built a chatbot or conversational AI product that went to real users
• Prompt engineering, RAG pipelines, embeddings, vector databases
• Integrated financial data APIs (Screener, Tickertape, NSE/BSE feeds, news APIs) into an application
• Fine-tuning / adapting LLMs where required
• Building ML models for user behaviour, recommendations, and financial insights
• End-to-end ownership: data exploration → modelling → deployment → monitoring

• Expectations :

• Understanding of large language models (LLMs) like LLAMA, Anthropic Claude 3, or Sonnet.
• Familiarity with cloud platforms for data science like AWS Bedrock and GCP Vertex AI
• Strong proficiency in Python and data science libraries (scikit-learn, TensorFlow, PyTorch).
• Solid understanding of statistical methods, machine learning algorithms, and wealth tech applications.
• Experience in data wrangling, visualization, and analysis.
• Collaborative mindset and ability to thrive in a fast-paced startup environment.

• Bonus points:
• Experience in capital market usecases
• Familiarity with recommender systems and personalization techniques.
• Experience building and deploying production models.
• Data science project portfolio or contributions to open-source libraries.
• Experience with embedding models and retrieval quality improvement
• Worked at an AI-first startup in any domain