Lead GCP MLOps Engineer

dentsu; Maharashtra; On-site; INR 2,800,000 - 5,200,000; Full time

Job summary

Merkle in Pune seeks a Senior GCP MLOps Engineer to automate deployment and lifecycle management of Python-based ML models. You will build and operate scalable ML pipelines on Google Cloud Platform, with emphasis on Vertex AI endpoints, monitoring, and reliability.

The role focuses on CI/CD automation, IaC with Terraform, and production-grade ML deployment patterns. You will collaborate with data science teams to ensure secure, scalable, and cost-efficient ML services in production.

Qualifications

• Bachelor's degree in CS, Engineering, IT, or related field.
• 5–8 years of cloud engineering, MLOps, or ML platform experience.
• Hands-on GCP experience with production ML deployments.
• Strong knowledge of Vertex AI and CI/CD for ML.

Responsibilities

• Lead MLOps platform engineering on GCP.
• Design and maintain scalable ML deployment pipelines.
• Automate deployment, testing, monitoring, and lifecycle management.
• Implement IaC and CI/CD for ML workloads.
• Ensure reliability, security, and cost efficiency of ML services.

Skills

GCP

MLOps

Vertex AI

Python

Education

Bachelor's degree in CS/Engineering

Tools

Terraform

GitHub Actions

Jenkins

GitLab CI/CD

Job description

• MLOps Platform Engineering
• Cloud Storage
• Cloud Build
• Cloud Run

Cloud Platform

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.

Job Description

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

• 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.
• 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.
• 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.
• 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.
• 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

• Cloud Platform

Skills / Technologies

• 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

• Bachelor's 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.

Location

Pune

Brand

Merkle

Time Type

Full time

Contract Type

Permanent