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Remote role plus a mid-level 3–6 year ML title increases candidate pool and competition.
Technical Databricks, MLOps, and governance skills transfer across industries but require ML domain knowledge.
Explicit 3–6 years plus mandatory Databricks, MLOps, governance, and security requirements enforce strict filtering.
Develop and productionize machine learning and Generative AI solutions across data lifecycle using Databricks Lakehouse architecture.
Build and maintain reliable, governed data pipelines with strong data quality, lineage (95% coverage), security, and compliance following defined SLOs (pipeline success 99.5%, MTTD 5min, MTTR 60min).
Optimize and automate ML/data workflows including CI/CD, monitoring, cost efficiency, and integration of semantic data layers for consistent business metrics.
3–6 years of experience in Data Science, Machine Learning, or ML Engineering with production deployment of models.
Proficiency in Python, SQL, Spark/PySpark and hands-on experience with Databricks ecosystem (Delta Lake, Unity Catalog, Jobs, Medallion architecture).
Experience with GenAI/LLM including prompt engineering, RAG, vector databases, and related operational considerations.
Strong knowledge of data governance, security (PII handling, RBAC/ABAC), compliance frameworks (SOC 2, ISO 27001, GDPR) and policy-as-code implementations.
Comfortable working end-to-end across data engineering, ML development, MLOps, data governance, and cloud platforms, with a focus on production reliability and measurable business impact.
Experience collaborating cross-functionally with Product, Engineering, and domain teams to define KPIs, design A/B tests, and deliver customer-facing AI/data products.
Proven ability to build scalable, cost-optimized data and ML infrastructure with strong emphasis on security, compliance, and operational excellence.