ML OPS
Listed on 2026-10-07
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IT/Tech
Machine Learning/ ML Engineer, AI Engineer (Applied/Software), Data Engineering, Cloud Computing: Infrastructure & Operations
Skills:
ML OPS
JD:We are looking for a skilled MLOps Engineer to join our team and help us build, deploy, and maintain robust and scalable machine learning systems. You will be responsible for the full lifecycle of our ML pipelines, from data ingestion to model serving. This is a hands-on role where you will design and implement automated workflows, ensure data quality, and manage model deployments in a production environment.
ResponsibilitiesData and Feature Pipelines
Design, build, and manage automated data ingestion, transformation, and validation pipelines using services like Kubeflow Pipelines and Vertex AI Pipelines.
Feature EngineeringImplement and containerize feature engineering logic for diverse datasets, ensuring reusability and scalability.
Data ValidationIntegrate and manage data validation processes, including leveraging advanced techniques like AI Agents and the Generative Language API to automatically detect and remediate data quality issues.
Model Training and Experimentation- Set up and maintain automated continuous training (CT) pipelines using Vertex AI Pipelines (Schedules) 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.
- Containerize ML models and their dependencies using Docker and manage images with Artifact Registry.
- Build and maintain CI/CD workflows for ML models, ensuring seamless and automated deployment.
- Configure and manage low-latency production serving environments using Vertex AI Endpoints for real-time inference.
- Strong experience with Google Cloud Platform (GCP) services, specifically in the MLOps and ML domain (Vertex AI, Kubeflow, Cloud Storage, 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 like Docker.
- Familiarity with CI/CD practices and pipeline automation.
- Knowledge of ML frameworks like Tensor Flow, and experience with experiment tracking and hyperparameter tuning.
- Excellent problem-solving skills and a strong understanding of the ML lifecycle.
- Experience with the Generative Language API (Gemini model) or other AI Agent integrations is a plus.
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