Generative AI Data Scientist – Fintech

Location: Bangalore,Hyderabad, Gurgaon

Experience: 4–7 years (1+ year in GenAI)

Company: KPMG

Employment Type: Full-Time

Notice period : Immedaite to 30 Days

Key Responsibilities

• Collaborate with business stakeholders and SMEs to define use cases and data strategies.
• Build PoCs/MVPs and operationalize them through production-ready deployments.
• Influence GenAI strategy in digital transformation projects across Fintech clients.
• Develop, deploy, and optimize LLM-based solutions using LangChain, LlamaIndex, and RAG frameworks.
• Engineer scalable GenAI pipelines on Azure/AWS/GCP with tools like Azure ML, Databricks, and Data Factory.
• Implement chunking, token management, hallucination control, and prompt engineering for high-performance LLM apps.
• Apply ML and DL algorithms (NLP, vision, simulation, forecasting) with tools like Python, TensorFlow, R, and SQL.
• Create and manage vector databases, knowledge graphs, and integrate Vision APIs.
• Develop robust, reusable, and scalable ML code using Docker, version control, and cloud-native best practices.
• Lead peer reviews and mentor team members while promoting knowledge sharing across geographies.
• Ensure delivery excellence across performance, security, reusability, and compliance using Responsible AI frameworks.

Required Qualifications

• B.E./B.Tech/B.Sc. with 4–7 years of experience in Data Science or Software Engineering.
• Minimum 1 year of hands-on GenAI experience with LLMs, RAG, agent frameworks, and cloud AI services.
• Strong programming skills in Python, SQL, and experience with ML frameworks.
• Proficient in feature engineering, hyperparameter tuning, and model evaluation.
• Deep understanding of ML algorithms, NLP, deep learning, optimization, and GenAI ecosystem.
• Experience with LangChain, LlamaIndex, Vector DBs, prompt engineering, and Vision APIs.
• Skilled in Agile methodologies, effective communication, and cross-functional leadership.

Must-Have Skills

LLMs, RAG Framework, Agent Frameworks, LangChain, Llama Index, Token Management, Chunking, Prompt Engineering, Vision APIs, Vector Databases, AI on Cloud, Knowledge Graphs, Open-Source AI Frameworks