AI/ML Architect

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.