Design and build end-to-end GenAI solutions for BFSI use cases (RAG, agents)
Develop and optimize data ingestion, chunking, embedding, and retrieval pipelines
Integrate domain and regulatory requirements into AI/ML solution design
Contribute to or implement data lineage-aware pipelines for traceability and audit readiness
Develop production-quality Python code and reusable AI/ML components
Perform model, prompt, and retrieval evaluation, monitoring, and performance tuning
Support client interactions, solution walkthroughs, and technical documentation
Mentor junior data scientists and act as a technical owner for assigned AI/GenAI modules

Requirements

Strong proficiency in Python and SQL, ML, and GenAI pipelines
Hands-on experience with GenAI / LLM-based systems (RAG, Fine Tuning, agents)
Solid understanding of retrieval-augmented generation (RAG) architectures and vector databases
Experience applying ML algorithms (classification, regression, ensembles) on structured BFSI data
BFSI domain knowledge (banking, risk, fraud, compliance etc)
Experience with LLM platforms and APIs (OpenAI, Azure OpenAI, AWS Bedrock, etc.)
Ability to design prompt strategies, guardrails, and evaluation metrics for GenAI systems
Understanding of data lineage, metadata, and traceability concepts in enterprise systems
Experience working on cloud-based data and AI platforms (AWS/Azure)
Strong problem-solving, communication, and stakeholder-facing skills in client environments.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

PythonSQLML algorithmsGenAI pipelinesretrieval-augmented generation (RAG)vector databasesclassificationregressionensemblesdata ingestion

problem-solvingcommunicationstakeholder-facingmentoringtechnical ownership