What you’ll work on:

Location: Bengaluru Experience: 4-8 Years What You’ll Work On - Building and owning a conversational AI research assistant for retail investors in Indian capital markets, think Claude but specialized for different instruments and macro analysis for non-expert users. - Designing multi-agent agentic pipelines with tool-calling, memory management, and multi-turn conversational flows that handle real, messy, incomplete queries from retail users (not just clean, structured prompts from analysts). - RAG pipeline architecture: source curation, chunking strategy, embedding quality, retrieval tuning, reranking, and citation-grounded responses that retail users can trust. - Integrating real-time financial data sources (NSE/BSE feeds, Screener, Tickertape, news APIs, company filings) as live tool-callable data layers, not just static retrieval. - Building the guardrails and evaluation layer: domain scoping, hallucination mitigation, confidence scoring, and monitoring to ensure the system stays accurate and within bounds over time. - Fine-tuning or adapting LLMs where retrieval alone isn't sufficient. - Building ML models for user behaviour, personalization, and financial insights that feed into the conversational layer. Expectations - 4–8 years of hands-on experience in Data Science, Machine Learning, or Applied AI. - Capital Markets/WealthTech domain experience is highly preferred, or candidates who have built AI research assistants for financial products or have strong personal knowledge of investing/trading. - 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. Good To Have - 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.