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

What you’ll work on:

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