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Tier-1 brand and metro location increase applicant density, but senior specialization reduces competition.
High; senior data engineering with ML/LLM evaluation focus requires specialized domain expertise.
High — explicit 9+ years, mandatory ML evaluation experience, and specific cloud/data engineering skills.
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Design and manage data architecture and pipelines for structured and unstructured data from multiple sources using cloud and local storage.
Develop automated data transformation tools leveraging ML techniques to maintain data quality and integrity continuously.
Lead design and implementation of automated AI agent evaluation pipelines, including metric definition, ground truth establishment, and reporting infrastructure.
9+ years experience building production data pipelines, data warehousing, and ETL/ELT systems with strong SQL, Python, and JavaScript or equivalent skills.
3+ years experience in ML systems, model validation, automated testing, or AI-powered applications with hands-on data quality and model monitoring.
Proven experience deploying and maintaining large-scale data systems in production environments, ensuring reliability and resolving data quality or latency issues.
Familiarity with cloud platforms (GCP, AWS), APIs, event-driven systems, and data governance practices; experience with AI/ML evaluation frameworks and LLM-based systems.
Experienced in leading cross-functional collaboration and mentoring engineers on data engineering and AI evaluation design.
Capable of taking end-to-end technical ownership and driving discussions across diverse teams autonomously.
Demonstrated ability to design scalable, secure data architectures suited for complex AI/ML agent workflows and evaluation pipelines.