Verified Job
IT / Software / Data Analyst
What you’ll work on
IT / Software / Data Analyst
#781816
Remote / WFH
HireVedaX
Job Description
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.
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.
