About the role

MLOps Engineer

Role Overview

The client is currently operating data workflows in GCP and is in the process of migrating only the Data Science workloads to Azure Databricks. Input data originates in GCP, data science workflows will execute in Azure Databricks, and model outputs are written back to GCP.

The MLOps Engineer will play a critical role in supporting model migration, building and optimizing MLOps workflows, and eventually contributing to broader data movement automation and self-service capabilities across cloud environments.

This role requires someone who deeply understands Databricks internals, PySpark, CI/CD orchestration, ML model operationalization, along with GCP and Azure knowledge.

Key Responsibilities

Model Migration & Optimization
- Support migration of existing ML models from GCP to Azure Databricks. Understand existing model architecture and replicate/optimize it in Azure Databricks.
- Work closely with the Data Science team to operationalize migrated models and further optimize to reduce compute cost and increase test coverage.
- Knowledge of Databricks infrastructure and Terraform.
MLOps & Workflow Orchestration
- Set up robust CI/CD pipelines using GitHub Actions for ML model deployments in Databricks.
- Implement and manage MLflow for tracking, versioning, and managing model lifecycle.
- Build efficient and scalable Data & ML pipelines using Databricks and PySpark.
Cloud & Data Movement Support
- Collaborate with the Data Engineering team regarding data movement between GCP and Azure Databricks.
- In the future, take over parts of cross-cloud data movement from the DE team and build self-service automation for data flows.
- Build pipelines where outputs from Azure Databricks must be transferred back to GCP.
Architecture & Best Practices
- Provide architectural inputs and workflow optimization guidance during and after migration.
- Ensure scalable, cost-efficient, and reliable model execution in Databricks.
- Improve testing, monitoring, and performance tuning for migrated and future ML models.

Required Experience & Skills

Must Have

6–8 years of experience in Data Engineering, ML Engineering, or MLOps roles.
Strong hands-on expertise in Databricks and deep understanding of how it works under the hood.
Proficiency in PySpark: writing scalable jobs, understanding execution plans, and optimization techniques.
Experience building CI/CD pipelines using GitHub Actions.
Experience with MLflow for tracking and operationalizing ML models.
Knowledge of integrating workflows between GCP and Azure ecosystems.
Strong debugging, optimization, and cost-efficiency mindset.

Good to Have

Experience with cross-cloud data movement patterns.
Familiarity with Data Science model structures and ability to collaborate closely with Data Science teams.
Exposure to model monitoring and alerts in a distributed/cloud environment.

Please Share Profiles With

Candidate Name	Current Organization
Total Experience	Relevant MLOps / Databricks Experience
Current CTC / Rate	Expected CTC / Rate
Notice Period	Current Location
Experience with Databricks / PySpark / MLflow	Experience with GCP – Azure Databricks Migration