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: 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 - 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 workplace 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 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 - 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: DGS India - Pune - Kharadi EON Free Zone Brand: Merkle Time Type: Full time Contract Type: Permanent#DGS .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.