ML OPS Engineer - Cynet Systems - San Jose, CA, US | Dice.com

Cynet Systems

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ML OPS Engineer

San Jose, CA, US • Posted 19 hours ago • Updated 1 hour ago

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Contract W2

On-site

Depends on Experience

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Job Details

Skills

API
Artificial Intelligence
Cloud Computing
Cloud Storage
Computer Science
Conflict Resolution
Continuous Delivery

Summary

We are looking for ML OPS Engineer for our client in San Jose, CA

Job Title: ML OPS Engineer

Job Location: San Jose, CA

Job Type: Contract

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 (Google Cloud Platform).
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 Google Cloud Platform 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.

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

Dice Id: 10516350
Position Id: CA_MOEL_1105
Posted 19 hours ago

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