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Strong Tier-1 brand increases applicant density despite niche agentic-AI and Databricks seniority.
Role requires deep ML/AI, Databricks, and production experience, limiting cross-industry transferability.
Explicit 10+ years, Databricks and agentic AI requirements make the hiring filters very strict.
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Design, build, and operationalize traditional ML models, agentic AI systems, and Databricks native data applications to drive business outcomes.
Own end-to-end delivery: data preparation, modeling, deployment, monitoring, and user-facing experiences in production ML and agent workflows.
Provide technical leadership including architectural decisions, mentoring, and ensuring ML rigor, engineering quality, and production readiness.
Bachelor's degree in Computer Science, Data Science, Machine Learning, Statistics, Mathematics, or related field.
5+ years of Data Science or related work experience; advanced degrees may substitute work experience (Master’s = 1 year, Doctorate = 2 years).
10+ years total experience in data science, ML engineering, or applied ML with ownership of production systems; 5+ years building RAG-based GenAI agentic applications.
Strong proficiency in Python, experience with traditional ML techniques, Databricks application development, feature engineering, model evaluation, and production deployment.
Senior-level practitioner combining data science, ML engineering, and full stack data application development expertise, with focus on production-grade solutions.
Experienced in agentic AI workflows and building multi-step agent pipelines integrating rules, ML models, and reasoning for complex business problems.
Familiar with ML lifecycle management including model monitoring, drift detection, retraining, and integrating ML outputs into enterprise applications using Databricks and cloud platforms.