Job Title: MLOps Engineer (6–8 Years Experience)
Location: Offshore (India)
Working Hours: 8 AM – 5 PM UK Time
Engagement 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 Overview
Our 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 migration
Building and optimizing MLOps workflows
Contributing to broader data movement automation and self-serve capabilities across cloud environments
This role requires deep expertise in Databricks , Py Spark , CI/CD orchestration , and ML model operationalization , along with strong familiarity with GCP and Azure.
Key Responsibilities
1. Model Migration & Optimization
Migrate 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 Orchestration
Implement robust CI/CD pipelines using Git Hub Actions for ML model deployments.
Utilize MLflow for tracking, versioning, and managing model lifecycle.
Develop scalable Data & ML pipelines using Databricks + Py Spark.
3. Cloud & Data Movement Support
Collaborate 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 Practices
Provide 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 Experience
6–8 years in Data Engineering, ML Engineering, or MLOps roles
Must-Have Skills
Strong hands-on expertise in Databricks , with deep understanding of its internals
Proficiency in Py Spark (scalable jobs, execution plans, optimization techniques)
CI/CD pipeline creation using Git Hub Actions
Experience with MLflow for tracking and operationalizing ML models
Knowledge of integrating workflows between GCP and Azure
Strong debugging, optimization, and cost-efficiency mindset
Good to Have
Experience with cross-cloud data movement
Familiarity with Data Science model structures and close collaboration with DS teams
