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Remote mid-level ML/GenAI role with broad Databricks and MLOps requirements increases applicant competition.
Role requires Databricks, Unity Catalog, governance and GenAI expertise, limiting transferability across industries.
Specific 3–6 years requirement and mandatory Databricks, GenAI, MLOps, security, and CI/CD experience increases filtering.
Build and enhance Databricks Lakehouse data pipelines with focus on governance, lineage, data quality, and security for reliable cross-product insights.
Develop, prototype, and productionize machine learning and GenAI solutions addressing customer and business problems, ensuring operational SLAs for pipeline success and incident response.
Engineer scalable, observable, and cost-optimized data and ML workflows incorporating CI/CD, security compliance, and FinOps principles.
3-6 years experience in Data Science, Machine Learning, or ML Engineering with production model deployment track record.
Proficient in Python, SQL, Spark/PySpark, and hands-on with Databricks platform including Delta Lake, Unity Catalog, and medallion architecture.
Experience in production ML lifecycle: feature engineering, model evaluation, monitoring, and GenAI techniques like prompt engineering and RAG.
Knowledge of data security and compliance practices including PII handling, RBAC/ABAC, policy-as-code, and experience with CI/CD for data and ML workloads using GitHub Actions and Databricks Asset Bundles.
Experienced ML/GenAI engineer comfortable working end-to-end from data engineering to production ML/AI solutions in a regulated, cloud SaaS environment.
Demonstrates strong operational focus with ability to meet stringent SLAs (e.g., 99.5% pipeline success, ≤5 min incident detection) and implement proactive monitoring and cost optimization.
Familiar with data governance, security compliance frameworks (SOC 2, ISO 27001, GDPR), and able to collaborate with multidisciplinary teams to align technical solutions with business outcomes.