Senior I ML/CV Engineer

• Production-grade Python and PySpark, and confident Spark SQL;
• Databricks at development and operations depth: jobs and orchestration with dependencies, job clusters and cluster policies, portable project bundles, source control integration;
• Unity Catalog: grants on tables and models, lineage, environment separation through catalogs;
• Working Delta Lake knowledge: table versions and time travel, change data feed, optimization, and how data layout affects feature read performance;
• MLflow: experiment tracking, model registry, version promotion, publishing models as endpoints;
• Feature storage and reuse, with consistent computation between training and inference;
• Hands-on experience operating models in production: monitoring, drift, retraining, incidents, rollback;
• Time-series and forecasting methods;
• Git, CI/CD, containers, secure handling of secrets.
• Built-in platform monitoring for data and model quality;
• Production ML on Azure and integration with cloud services;
• Understanding of the platform consumption model and cost tuning for recurring jobs;
• Geospatial features and route data;
• Environments where a model must be explainable to business owners and auditors;
• Transportation, logistics or supply chain.

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Originally posted on Himalayas