TC
MLOps / AI/ML architect
TechDigital Corporation
🇺🇸 United States
On-site
4 months ago
- ADK
- Python
- MLOps
- Machine Learning
- Kubeflow
- Vertex AI
- AI
- Docker
- CI/CD
- GCP
- TensorFlow
- Gemini
4 months ago
We are looking for a skilledMLOps Architect 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.
Responsibilities
- Data and Feature Pipelines: Design, build, and manage automated data ingestion, transformation, and validation pipelines using services likeKubeflow Pipelines andVertex AI Pipelines.
- Feature Engineering: Implement and containerize feature engineering logic for diverse datasets, ensuring reusability and scalability.
- Data Validation: Integrate and manage data validation processes, including leveraging advanced techniques likeAI Agents and theGenerative Language API to automatically detect and remediate data quality issues.
- Model Training and Experimentation:
- Set up and maintain automated continuous training (CT) pipelines usingVertex AI Pipelines (Schedules) andCloud Scheduler.
- Implementexperiment tracking to log and compare model parameters, metrics, and artifacts.
- Configure and executeHyperparameter Tuning jobs usingVertex AI Training to optimize model performance.
- Model Management: Establish a robustModel Versioning system to manage and store model artifacts securely in a centralized repository (Cloud Storage).
- Deployment and Serving:
- Containerize ML models and their dependencies usingDocker and manage images withArtifact Registry.
- Build and maintainCI/CD workflows for ML models, ensuring seamless and automated deployment.
- Configure and manage low-latency production serving environments usingVertex 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 likeDocker.
- Familiarity with CI/CD practices and pipeline automation.
- Knowledge of ML frameworks likeTensorFlow, and experience with experiment tracking and hyperparameter tuning.
- Excellent problem-solving skills and a strong understanding of the ML lifecycle.
MLOps / AI/ML architect · TechDigital Corporation