Job Title

Job Title: MLOps Engineer (6–8 Years Experience)Location: Offshore (India)Working Hours: 8 AM – 5 PM UK TimeEngagement Overview:The initial focus will be on migrating existing Data Science models over the next few months, followed by active involvement in Data Science model creation, operations, and enhancements.Role OverviewOur client operates data workflows in GCP and is migrating its Data Science workloads to Azure Databricks. Input data will continue to originate in GCP, while workflows will be executed in Azure Databricks, and model outputs written back to GCP.The MLOps Engineer will be pivotal in:Supporting model migrationBuilding and optimizing MLOps workflowsContributing to broader data movement automation and self-serve capabilities across cloud environmentsThis role requires deep expertise in Databricks, PySpark, CI/CD orchestration, and ML model operationalization, along with strong familiarity with GCP and Azure.Key Responsibilities1. Model Migration & OptimizationMigrate existing ML models from GCP to Azure Databricks; replicate and optimize architecture.Work closely with the Data Science team to operationalize and optimize migrated models for cost efficiency and testing coverage.2. MLOps & Workflow OrchestrationImplement robust CI/CD pipelines using GitHub Actions for ML model deployments.Utilize MLflow for tracking, versioning, and managing model lifecycle.Develop scalable Data & ML pipelines using Databricks + PySpark.3. Cloud & Data Movement SupportCollaborate with Data Engineering for GCP → Azure Databricks data workflows.Take ownership of cross-cloud data movement and build self-serve automation for pipelines.Ensure Azure Databricks outputs are seamlessly transferred back to GCP.4. Architecture & Best PracticesProvide architecture and workflow optimization guidance during and post migration.Ensure scalable, reliable, and cost-efficient model execution in Databricks.Enhance testing, monitoring, and performance tuning of ML models.Required Experience6–8 years in Data Engineering, ML Engineering, or MLOps rolesMust-Have SkillsStrong hands-on expertise in Databricks, with deep understanding of its internalsProficiency in PySpark (scalable jobs, execution plans, optimization techniques)CI/CD pipeline creation using GitHub ActionsExperience with MLflow for tracking and operationalizing ML modelsKnowledge of integrating workflows between GCP and AzureStrong debugging, optimization, and cost-efficiency mindsetGood to HaveExperience with cross-cloud data movementFamiliarity with Data Science model structures and close collaboration with DS teamsExposure to model monitoring and alerts in distributed/cloud environments