About the role

MLOps Engineer

Engagement Mode: Remote

Timezone: India

Position Overview

The MLOps Engineer will play a critical role in supporting the migration of Data Science models from GCP to Azure Databricks, building and optimizing MLOps workflows, and eventually contributing to broader data movement automation and self-serve capabilities across cloud environments. The client is currently operating data workflows in GCP and is migrating only the Data Science workloads to Azure Databricks, while input data originates in GCP and model outputs are written back to GCP. This role requires someone who deeply understands Databricks internals, PySpark, CI/CD orchestration, and ML model operationalization, along with knowledge of GCP and Azure. Working hours are 8 AM – 5 PM UK Time.

Key Responsibilities

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
Optimize models to reduce compute cost and increase test coverage
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 + PySpark
Collaborate with the Data Engineering team regarding data movement between GCP ↔ Azure Databricks
Take over parts of cross-cloud data movement from the DE team and build self-serve automation for data flows
Build pipelines where outputs from Azure Databricks must be transferred back to GCP
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 Skills

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

Preferred (Bonus) Skills

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