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docker kubernetes spark mlops feature engineering📋 Description
- Design, build, and deploy enterprise ML systems with audit/monitoring.
- Maintain ML infra across hybrid/multi-cloud environments.
- Collaborate with cross-functional teams to align ML with business goals.
- Implement data pipelines with Spark, Azure Databricks, MLflow, Kubernetes, Docker.
- Ensure governance, observability, and performance KPIs for ML apps.
- Drive continuous improvement and knowledge sharing across teams.
🎯 Requirements
- Bachelor's degree required; Master's preferred in CS, Engineering, or related.
- 5–8+ years in ML Engineering, MLOps, or high-scale ML systems.
- Deep expertise in Spark, Azure Databricks, MLflow, Kubernetes, and Docker.
- Proven track record deploying ML at enterprise scale with audit and monitoring layers.
- Familiarity with hybrid/multi-cloud infrastructure.
- Nice-to-have: AI tooling proficiency; ML platform leadership; feature stores; AutoML; H2O.
🎁 Benefits
- A High-Impact Environment
- Commitment to Professional Development
- Flexible and Collaborative Culture
- Global Opportunities
- Vibrant Community
- Total Rewards
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