MLOps Engineer (Google Cloud Platform)

Position Overview

We are seeking a highly skilled MLOps Engineer with strong Google Cloud Platform (Google Cloud Platform) experience to design, build, and maintain scalable machine learning infrastructure and pipelines. This role will closely collaborate with Data Scientists, Data Engineers, and DevOps teams to enable reliable, secure, and production-grade ML systems on Google Cloud Platform.

Key Responsibilities

• Design, deploy, and manage end-to-end ML pipelines on Google Cloud Platform using Vertex AI, AI Platform, Cloud Run, and Cloud Composer (Managed Airflow).

• Build and maintain scalable infrastructure using GKE, Compute Engine, Cloud Storage, and related Google Cloud Platform services.

• Automate data ingestion, model training, validation, deployment, and retraining workflows using CI/CD pipelines integrated with GitLab and Bitbucket.

• Implement monitoring and logging solutions using Google Cloud Monitoring and Cloud Logging to track system health, model performance, and data drift.

• Collaborate with cross-functional teams to streamline the ML lifecycle from experimentation to production.

• Manage model, dataset, and code versioning using Artifact Registry, Cloud Storage, and Git-based repositories.

• Optimize model performance and infrastructure costs using Docker, Kubernetes (GKE), GPTPUs, and preemptible VMs.

• Ensure security, compliance, and data privacy using Cloud IAM, VPC Service Controls, and DLP.

• Integrate Google Cloud Platform-native tools and open-source MLOps frameworks such as MLflow and Kubeflow.

• Support deployment of Generative AI and RAG-based applications on Google Cloud Platform.

Required Qualifications

Technical Skills

• Strong programming experience in Python.

• Hands-on experience with Google Cloud Platform services:

  • Vertex AI, GKE, Cloud Run

  • BigQuery, Cloud Storage

  • Cloud Composer (Managed Airflow)

  • Dataproc / PySpark

• Experience with Infrastructure as Code (Terraform).

• Strong knowledge of Docker, Kubernetes (GKE), and CI/CD pipelines.

• Experience with GitLab and Bitbucket repositories.

• Familiarity with ML frameworks: TensorFlow, PyTorch, scikit-learn.

• Experience with MLOps tools: MLflow, Kubeflow.

• Solid understanding of data engineering concepts, ETL pipelines, BigQuery, Dataflow, and PySpark.

• Exposure to GenAI, LLMs, and RAG architectures.

Soft Skills

• Strong analytical and problem-solving skills.

• Excellent communication and stakeholder collaboration abilities.

• Ability to work effectively in a fast-paced, cross-functional environment.

Preferred Qualifications

• Experience with large-scale distributed ML systems (Vertex AI Pipelines, Kubeflow on GKE).

• Hands-on experience with Feature Stores.

• Knowledge of model monitoring, data drift detection, and model performance degradation.

• Familiarity with microservices architecture and API development using Cloud Functions or Cloud Endpoints.

• Google Cloud certifications:

  • Professional Machine Learning Enginee