Lead GCP MLOps Engineer

The purpose of this role is to provide technical guidance and suggest improvements in development processes. Develop required software features, achieving timely delivery in compliance with the performance and quality standards of the company.

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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