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Strong Fidelity brand and Bengaluru metro raise competition, but senior niche role reduces applicant pool.
Finance-specific investment-data provenance and governance plus AI retrieval expertise reduce cross-industry portability.
Extensive mandatory tech stack and specialized AI/data engineering skills create rigorous filtering.
Design, build, test, deploy and operate production-grade data pipelines supporting machine learning, LLM, retrieval-augmented generation and AI workflows in investment research and sustainability domains.
Create AI-ready datasets including feature, training, evaluation sets, embeddings, vector indexes, and govern the delivery of trusted data for semantic and vector search applications.
Collaborate with AI/ML engineers and investment stakeholders to produce data services, ensure data governance, quality, security, and translate AI use cases into production data products.
Strong hands-on experience in data engineering for AI/ML use cases, including LLM and retrieval-augmented generation pipelines.
Proficiency in Python, Spark, SQL, and experience with Snowflake, AWS, Kafka, plus enterprise data sources like Oracle or Microsoft SQL Server.
Demonstrated ability to own data engineering components end-to-end: design, build, deployment, monitoring, and production support.
Work Experience Required: Hands-on production data engineering experience delivering AI/ML data pipelines and data foundations (exact years not explicitly mentioned).
Expertise in building embedded pipelines and vector search applications tied to investment research or sustainability data.
Capable of implementing and supporting secure, governed production data workflows integrating modern and enterprise platforms.
Excellent at technical ownership, root-cause analysis, cross-functional collaboration with AI/ML engineers and business stakeholders, and communicating complex data topics clearly.