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Large employer and Bangalore metro increase applicant density, but senior GenAI specialization moderates competition.
GenAI platform, data engineering, and API skills are broadly transferable across industries.
Explicit 10+ years, mandatory GenAI, data engineering, and vector DB skills make shortlisting highly strict.
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Architect and implement GenAI-powered workflows and Retrieval-Augmented Generation pipelines over enterprise data sources to automate audit and compliance documentation.
Design and build data pipelines, models, and semantic layers integrating data lakes, compliance systems, and document repositories ensuring data quality, lineage, and governance.
Lead full stack platform development including UI, APIs, and backend microservices to support AI-driven research workflows, document generation, and monitoring dashboards.
10+ years of experience in Data Engineering, Software Engineering, or AI roles.
Strong expertise in Python programming and experience with Generative AI, LLMs, Agentic AI frameworks.
Proficient in data engineering including ETL/ELT pipelines, data modeling, SQL large-scale processing, and hands-on with APIs and distributed systems.
Experience with cloud data platform tools such as AWS Glue, Azure Data Factory, Databricks, Snowflake, and knowledge of data governance and quality frameworks.
Experienced in integrating AI and data engineering to build scalable, enterprise-grade GenAI platforms for compliance and risk management.
Skilled in end-to-end full stack development with ability to lead design decisions and mentor engineering teams.
Familiar with enterprise security, regulatory requirements, and governance in banking or financial services domain preferred but not mandatory.