MLOps Engineer — Build, Deploy & Scale Pipelines

TechDigital Group; San Jose (CA); On-site; USD 140,000 - 190,000; Full time

Job summary

TechDigital Group is seeking an experienced MLOps Engineer to design, deploy, and maintain scalable ML systems in production. You will own the full lifecycle of ML pipelines from data ingestion to model serving.

Responsibilities include building data and feature pipelines on Kubeflow and Vertex AI, containerizing models with Docker, implementing CI/CD for ML workflows, and monitoring data quality and experiment tracking in a production environment.

Qualifications

• Experience building end-to-end ML pipelines and data workflows.
• Hands-on with Kubeflow, Vertex AI Pipelines, and Google Cloud Platform.
• Containerization with Docker and deployment of ML models.
• CI/CD practices for ML workflows and automation.
• Experience with TensorFlow and ML experiment tracking.

Responsibilities

• Design, build, and manage automated data ingestion, transformation, and validation pipelines.
• Containerize ML models and dependencies with Docker; manage images.
• Implement and monitor CI/CD pipelines for ML models and datasets.
• Define and run hyperparameter tuning and experiment tracking workflows.
• Deploy real-time inference endpoints and maintain scalable serving stacks.

Skills

ML OPS

Docker

Kubeflow

Vertex AI

TensorFlow

CI/CD

Tools

Kubeflow

Vertex AI

Docker

Artifact Registry

Cloud Storage

TensorFlow

Job description

TechDigital Group is seeking an experienced MLOps Engineer to design, deploy, and maintain scalable ML systems in production. You will own the full lifecycle of ML pipelines from data ingestion to model serving.

Responsibilities include building data and feature pipelines on Kubeflow and Vertex AI, containerizing models with Docker, implementing CI/CD for ML workflows, and monitoring data quality and experiment tracking in a production environment.