MLOps Engineer – Vertex AI Specialist

The MLOps Engineer – Vertex AI Specialist will build and maintain end-to-end MLOps pipelines using Vertex AI, Kubeflow, and related GCP services. This role involves optimizing training/inference, setting up CI/CD with Cloud Build, and monitoring deployed models. Candidates need 5-10 years in software/data engineering, with 3+ years in MLOps on GCP, and strong skills in ML lifecycle, DevOps, and cloud-native technologies.

Must Have

• Build and maintain end-to-end MLOps pipelines using Vertex AI Pipelines, Vertex AI Experiments, and Vertex AI Model Monitoring
• Implement Kubeflow Pipelines for scalable and reproducible ML workflows
• Utilize Managed Notebooks and Vertex AI Datasets for data preprocessing and model development
• Optimize training and inference using GPU accelerators and CUDA
• Set up Cloud Build for automated CI/CD of ML models and manage artifacts via Artifact Registry
• Monitor deployed models in real time using Vertex AI Monitoring and implement drift detection strategies
• Collaborate with data scientists and DevOps teams to streamline deployment and lifecycle management
• Ensure operational excellence through data versioning, model version control, and infrastructure automation
• Bachelor’s or Master’s degree in Computer Science, Data Engineering, or related field
• 5–10 years of experience in software/data engineering
• 3+ years in MLOps or ML engineering on GCP using Vertex AI
• Hands-on experience deploying ML models in production environments
• Strong understanding of ML lifecycle, DevOps, and cloud-native technologies
• Excellent scripting and automation skills
• Proficiency in MLOps Tools: MLflow, DVC, Kubeflow, TFX
• Experience with CI/CD & DevOps: Jenkins, GitHub Actions, Docker, Kubernetes, Azure DevOps, Cloud Build
• Programming skills: Python, ML pipeline automation, Bash, YAML
• Cloud Platforms: Azure ML, AWS SageMaker, GCP Vertex AI
• Monitoring & Logging: Prometheus, Grafana, ELK/EFK stack
• Model Deployment: FastAPI, Flask, REST APIs, containerized delivery
• Data Engineering: Airflow, Spark, Delta Lake, feature stores
• Security & Compliance: IAM, Key Vault, encryption, GDPR/SOC2

Good to Have

• AutoML Tools: Azure AutoML, H2O.ai, Google AutoML
• Edge AI: Deployment on IoT/edge devices
• Visualization: Power BI, Streamlit, Dash
• Domain Exposure: Manufacturing, BFSI, Healthcare, Retail
• Responsible AI: Model explainability, fairness, bias detection

Roles & Responsibilities

• Build and maintain end-to-end MLOps pipelines using Vertex AI Pipelines, Vertex AI Experiments, and Vertex AI Model Monitoring.
• Implement Kubeflow Pipelines for scalable and reproducible ML workflows.
• Utilize Managed Notebooks and Vertex AI Datasets for data preprocessing and model development.
• Optimize training and inference using GPU accelerators and CUDA.
• Set up Cloud Build for automated CI/CD of ML models and manage artifacts via Artifact Registry.
• Monitor deployed models in real time using Vertex AI Monitoring and implement drift detection strategies.
• Collaborate with data scientists and DevOps teams to streamline deployment and lifecycle management.
• Ensure operational excellence through data versioning, model version control, and infrastructure automation.

Experience & Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, or related field.
• 5–10 years of experience in software/data engineering, with 3+ years in MLOps or ML engineering on GCP using Vertex AI.
• Hands-on experience deploying ML models in production environments.
• Strong understanding of ML lifecycle, DevOps, and cloud-native technologies.
• Excellent scripting and automation skills.

Primary Skills (Mandatory)

• MLOps Tools: MLflow, DVC, Kubeflow, TFX
• CI/CD & DevOps: Jenkins, GitHub Actions, Docker, Kubernetes, Azure DevOps, Cloud Build
• Programming: Python, ML pipeline automation, Bash, YAML
• Cloud Platforms: Azure ML, AWS SageMaker, GCP Vertex AI
• Monitoring & Logging: Prometheus, Grafana, ELK/EFK stack
• Model Deployment: FastAPI, Flask, REST APIs, containerized delivery
• Data Engineering: Airflow, Spark, Delta Lake, feature stores
• Security & Compliance: IAM, Key Vault, encryption, GDPR/SOC2

Secondary Skills (Good to Have)

• AutoML Tools: Azure AutoML, H2O.ai, Google AutoML
• Edge AI: Deployment on IoT/edge devices
• Visualization: Power BI, Streamlit, Dash
• Domain Exposure: Manufacturing, BFSI, Healthcare, Retail
• Responsible AI: Model explainability, fairness, bias detection