Lead ML Devops Engineer

Job Description:

Job Title: Lead GCP MLOps Engineer

DCF: L35

Experience: 5 - 8 Years

Role Summary

We are seeking a highly skilled Senior GCP MLOps Engineer to support the deployment automation and operationalization of machine learning solutions on Google Cloud Platform (GCP).

The primary focus of this role is to automate the deployment and lifecycle management of Python-based machine learning models developed by business and data science teams. The ideal candidate will possess strong expertise in GCP cloud engineering MLOps frameworks CI/CD automation infrastructure management and production-grade ML deployment architectures.

This is an engineering-focused role responsible for ensuring machine learning models are deployed monitored scalable secure and reliable in production environments.

Key Responsibilities

1. MLOps Platform Engineering

• Design build and maintain scalable MLOps frameworks on Google Cloud Platform.
• Automate deployment testing monitoring and lifecycle management of machine learning models.
• Establish repeatable and standardized ML deployment processes across environments.
• Implement model versioning artifact management and deployment governance standards.
• Support model retraining rollback and release management processes.

2. Machine Learning Deployment & Automation

• Deploy Python-based machine learning models into production environments.
• Build automated deployment pipelines for batch and real-time inference workloads.
• Develop reusable deployment templates and automation frameworks.
• Support model serving using Vertex AI Endpoints and containerized deployment architectures.
• Ensure high availability reliability and scalability of production ML services.

3. CI/CD & Infrastructure Automation

• Design and implement CI/CD pipelines for machine learning applications and services.
• Integrate source control testing and deployment workflows into enterprise delivery pipelines.
• Implement Infrastructure-as-Code (IaC) practices for repeatable environment provisioning.
• Support environment management across development testing and production environments.

4. Cloud Engineering & Platform Operations

• Design and support cloud-native ML infrastructure on GCP.
• Manage and optimize services including:
  • Vertex AI
  • Cloud Storage
  • BigQuery
  • Cloud Build
  • Cloud Run
  • Kubernetes Engine (GKE)
  • Pub/Sub
• Optimize infrastructure for performance reliability security and cost efficiency.
• Troubleshoot production issues and support platform stability initiatives.

5. Monitoring Observability & Governance

• Implement monitoring and alerting frameworks for deployed machine learning services.
• Track model performance operational health latency and system utilization.
• Support model lifecycle governance and operational compliance requirements.
• Establish logging observability and operational dashboards.
• Drive best practices for production support and operational excellence.

Technical Expertise Required

Area

Skills / Technologies

Cloud Platform

Google Cloud Platform (GCP)

MLOps

Vertex AI Model Deployment Model Monitoring ML Lifecycle Management

Programming

Python

CI/CD

Cloud Build GitHub Actions Jenkins GitLab CI/CD

Infrastructure Automation

Terraform Infrastructure-as-Code

Data Platforms

BigQuery Cloud Storage

Messaging & Integration

Pub/Sub APIs

Monitoring & Observability

Cloud Monitoring Logging Alerting

Version Control

Git GitHub

Qualifications

• Bachelors degree in Computer Science Engineering Information Technology or a related discipline.
• 5 - 8 years of experience in Cloud Engineering MLOps or ML Platform Engineering.
• Strong hands-on experience with Google Cloud Platform (GCP).
• Proven experience deploying and operationalizing Python-based machine learning models.
• Strong experience with Vertex AI and production ML deployment patterns.
• Experience building CI/CD pipelines for machine learning applications.
• Experience implementing Infrastructure-as-Code using Terraform or similar tools.
• Experience monitoring and supporting production machine learning workloads.
• Strong troubleshooting and problem-solving skills.

Preferred Qualifications

• Google Cloud Professional Machine Learning Engineer Certification.
• Familiarity with MLflow Kubeflow or similar MLOps frameworks.

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