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Senior, niche MLOps role reduces applicant density despite metro location and broad skill requirements.
MLOps skills transfer across industries, though some insurance-specific data experience may be preferred.
Multiple explicit years and mandatory tool experience create strict technical filtering for candidates.
Lead development and maintenance of robust data pipelines for structured and unstructured data supporting ML model building.
Manage and optimize MLOps infrastructure including Databricks platform, Delta Lake, and related cloud data warehousing technologies.
Develop and maintain common data engineering tools, libraries, and APIs to support multiple teams and ensure software engineering best practices.
Bachelor’s or master’s degree in Computer Science, Data Science, Engineering, or related field.
8+ years of overall experience including at least 4+ years with Python, SQL, PySpark, and bash scripting.
3+ years experience with Cloud Data Warehousing platforms (Redshift, Snowflake, Databricks SQL) and distributed frameworks like Spark.
2+ years hands-on experience with Databricks platform and familiarity with CI/CD (Jenkins), version control (GitHub/Bitbucket), and orchestration tools (Airflow/Prefect).
Experience working extensively on end-to-end MLOps solutions combining software engineering and data engineering capabilities.
Proven ability to build and maintain scalable, reusable tools and APIs across multiple teams within an organization.
Strong expertise in cloud-based data architectures and hands-on usage of Databricks and related technologies aligning with data science workflows.