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ML OPS Engineer
ML OPS EngineerCynet Systems • San Jose, CA, United States
ML OPS Engineer

ML OPS Engineer

Cynet Systems • San Jose, CA, United States
2 days ago
Job type
  • Full-time
Job description

Job Description :

Pay Range : $65hr - $70hr

  • The MLOps Engineer is responsible for designing, building, and managing end-to-end machine learning pipelines on Google Cloud Platform (GCP).
  • This role covers automated data ingestion, feature engineering, model training, experimentation, deployment, and serving in production environments.
  • The MLOps Engineer ensures scalable, reliable, and efficient ML workflows while leveraging cutting-edge tools and technologies.

Responsibilities :

  • Design, build, and manage automated data ingestion, transformation, and validation pipelines using services like Kubeflow Pipelines and Vertex AI Pipelines.
  • Implement and containerize feature engineering logic for diverse datasets, ensuring reusability and scalability.
  • Integrate and manage data validation processes, including using AI Agents and Generative Language APIs to detect and remediate data quality issues.
  • Set up and maintain automated continuous training (CT) pipelines using Vertex AI Pipelines and Cloud Scheduler.
  • Implement experiment tracking to log and compare model parameters, metrics, and artifacts.
  • Configure and execute Hyperparameter Tuning jobs using Vertex AI Training to optimize model performance.
  • Establish a robust Model Versioning system to manage and store model artifacts securely in Cloud Storage.
  • Containerize ML models and their dependencies using Docker and manage images with Artifact Registry.
  • Build and maintain CI / CD workflows for ML models to ensure seamless and automated deployment.
  • Configure and manage low-latency production serving environments using Vertex AI Endpoints for real-time inference.

    • Skills :

  • Strong experience with GCP services, specifically Vertex AI, Kubeflow, Cloud Storage, and Artifact Registry.
  • Proven ability to design and implement end-to-end ML pipelines for data management, model training, and deployment.
  • Hands-on experience with containerization technologies such as Docker.
  • Familiarity with CI / CD practices and pipeline automation.
  • Knowledge of ML frameworks like TensorFlow and experience with experiment tracking and hyperparameter tuning.
  • Excellent problem-solving skills and strong understanding of the ML lifecycle.
  • Experience with Generative Language API (Gemini model) or other AI Agent integrations is a plus.

    • Qualification And Education :

  • Bachelor's degree in Computer Science, Data Science, Engineering, or a related field; advanced degree preferred.
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