AI/ML Architect

• 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.