What you’ll work on:
HireVeda
- Location
- Bengaluru, Karnataka, India
- Job type
- Full-time
Required skills
- Python
- AWS
- capital markets
- data science
- GCP
- machine learning
- TensorFlow
- Pytorch
- Vertex
About the role
HireVeda
Website:
hireveda.com
Job details:
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.
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