We are looking for AI/ML Architect for our client in San Jose, CA
Job Title: AI/ML Architect
Job Location: San Jose, CA
Job Type: Contract
Job Overview:
Pay Range: $55hr - $60hr
- Responsible for designing, implementing, and managing end-to-end MLOps pipelines, machine learning infrastructure, and scalable AI solutions on Google Cloud Platform.
Requirement/Must Have:
- Strong experience with Google Cloud Platform services in the MLOps and machine learning domain.
- Experience with Vertex AI, Kubeflow, Cloud Storage, and Artifact Registry.
- Proven ability to design and implement end-to-end machine learning pipelines for data management, model training, and deployment.
- Hands-on experience with Docker containerization technologies.
- Familiarity with CI/CD practices and automation pipelines.
- Knowledge of machine learning frameworks such as TensorFlow.
- Strong understanding of the machine learning lifecycle and MLOps best practices.
Responsibilities:
- Design, build, and manage automated data ingestion, transformation, and validation pipelines using Kubeflow Pipelines and Vertex AI Pipelines.
- Implement scalable and reusable feature engineering pipelines for diverse datasets.
- Containerize feature engineering logic and machine learning workflows.
- Integrate and manage data validation processes to detect and remediate data quality issues.
- Utilize AI Agents and Generative Language API capabilities to improve data validation and automation workflows.
- Set up and maintain continuous training pipelines using Vertex AI Pipelines and Cloud Scheduler.
- Implement experiment tracking to monitor model parameters, metrics, and artifacts.
- Configure and execute hyperparameter tuning jobs using Vertex AI Training services.
- Establish model versioning systems and manage model artifacts in centralized repositories using Cloud Storage.
- Containerize machine learning models and dependencies using Docker.
- Manage container images using Artifact Registry.
- Build and maintain CI/CD workflows for machine learning deployment automation.
- Configure and manage low-latency production inference environments using Vertex AI Endpoints.
- Ensure scalable, reliable, and efficient model serving for real-time inference workloads.
Should Have:
- Strong analytical and problem-solving skills.
- Ability to work with complex machine learning systems and cloud-native architectures.
- Strong collaboration and communication skills.
- Experience with Generative Language API, Gemini models, or AI Agent integrations preferred.
Skills:
- Google Cloud Platform (Google Cloud Platform).
- Vertex AI and Kubeflow Pipelines.
- Cloud Storage and Artifact Registry.
- Docker containerization.
- CI/CD pipeline automation.
- TensorFlow.
- Hyperparameter tuning and experiment tracking.
- Data validation and feature engineering.
- Model deployment and real-time inference.
- MLOps and machine learning lifecycle management.
Qualification And Education:
- Bachelor s degree in Computer Science, Data Science, Artificial Intelligence, or related field preferred.