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docker kubernetes spark mlops mlflow📋 Description
- Architect end-to-end ML infrastructure across pipelines, serving, monitoring, and governance.
- Lead deployment of high-impact models (forecasting engines, optimization solvers, NLP models).
- Design advanced CI/CD workflows using Azure Pipelines, MLflow, and Databricks.
- Implement model registry, versioning, lineage, and audit compliance.
- Build monitoring systems for model drift and retraining automation.
- Mentor MLOps engineers and guide cross-functional platform integration.
🎯 Requirements
- 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.
- Familiarity with hybrid/multi-cloud infrastructure.
- AI tooling proficiency and leadership experience in ML platform or DevOps teams.
- Experience with feature stores and feature engineering; AutoML is a plus.
🎁 Benefits
- A High-Impact Environment
- Commitment to Professional Development
- Flexible and Collaborative Culture
- Global Opportunities
- Vibrant Community
- Total Rewards
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