Advanced MLOps Developer

6 days ago

Aurangābad, India beBeeMl Full time

Machine Learning Operations SpecialistWe are seeking a highly skilled Machine Learning Operations Specialist to design, build, and manage scalable machine learning operations on the Databricks Lakehouse Platform. The role involves automating ML workflows, operationalizing models, enabling reproducible pipelines, and ensuring governance and monitoring across the ML lifecycle.Key ResponsibilitiesDevelop Scalable MLOps Pipelines: Build automated ML pipelines for training, validation, deployment, and batch/real-time inference using Databricks Workflows, Jobs, Repos, and Delta Live Tables where applicable. Implement distributed training and inference pipelines using MLflow + PySpark.Model Lifecycle Management: Manage model versioning and promotion across dev → staging → production using MLflow Model Registry. Create reproducible workflows for model packaging, deployment, and rollback.CI/CD Integration: Build and integrate ML pipelines with CI/CD using Azure DevOps, GitHub Actions, or Jenkins. Automate testing, validation, and deployment for ML artifacts, notebooks, and infrastructure.Feature Engineering & Data Pipelines: Collaborate with Data Engineering teams to build optimized Delta Lake pipelines (Bronze/Silver/Gold architecture). Implement feature engineering workflows and support feature reuse at scale.Monitoring & Governance: Set up model monitoring for performance, drift, data quality, and lineage using Databricks-native tools, MLflow metrics, and cloud monitoring services (Azure/AWS). Ensure compliance through logging, auditing, permissions, and environment governance.Cross-Functional Collaboration: Work closely with Data Scientists, Data Engineers, Cloud teams, and Product teams. Document workflows, best practices, and MLOps reusable components.Required Skills & QualificationsStrong hands-on experience with Databricks (Workflows, Repos, Jobs, Compute)Proficiency with MLflow (Tracking, Registry, Model Deployment)Expertise in Delta Lake, PySpark, and distributed data pipelinesSolid programming skills in Python and SQLExperience with CI/CD tools: Azure DevOps, GitHub Actions, JenkinsFamiliarity with cloud platforms: Azure, AWS, or GCPUnderstanding of containerization (Docker) and orchestration (Kubernetes)Background in ML model training, serving, and observability

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