Ai/ML Software Development SME

Job Title

Pay Range: $70hr - $75hr

Requirement/Must Have

• Strong experience with Google Cloud Platform services in the MLOps and machine learning domain.
• Hands-on expertise with Vertex AI, Kubeflow, Cloud Storage, and Artifact Registry.
• Proven ability to design and implement end-to-end machine learning pipelines for data ingestion, feature engineering, model training, and deployment.
• Experience building and managing automated data ingestion, transformation, and validation pipelines.
• Experience with Vertex AI Pipelines and Kubeflow Pipelines.
• Strong hands-on experience with containerization technologies such as Docker.
• Experience building and maintaining CI/CD workflows for machine learning models.
• Experience with low-latency model serving and production inference environments.
• Experience with Vertex AI Endpoints for real-time inference.
• Familiarity with CI/CD practices and pipeline automation.
• Knowledge of machine learning frameworks such as TensorFlow.
• Experience with experiment tracking and hyperparameter tuning.
• Strong problem-solving skills and deep understanding of the machine learning lifecycle.

Experience

• Experience with continuous training pipelines using Vertex AI Pipelines, scheduling, and Cloud Scheduler preferred.
• Experience with model versioning and artifact management in Cloud Storage preferred.
• Experience with feature engineering for diverse datasets preferred.
• Experience with Generative Language API (Gemini model) or other AI Agent integrations is a plus.
• Experience with automated data quality validation and remediation processes preferred.

Responsibilities

• Design, build, and manage automated data ingestion, transformation, and validation pipelines using Kubeflow Pipelines and Vertex AI Pipelines.
• Implement and containerize feature engineering logic for diverse datasets to ensure scalability and reusability.
• Integrate and manage data validation processes, including automated detection and remediation of data quality issues.
• Leverage advanced techniques such as AI Agents and Generative Language API for data quality management.
• Set up and maintain automated continuous training pipelines using Vertex AI Pipelines 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.
• Establish and maintain a model versioning system to securely manage and store model artifacts in 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 models to enable seamless and automated deployments.
• Configure and manage low-latency production serving environments using Vertex AI Endpoints for real-time inference.
• Support the full lifecycle of machine learning pipelines from data ingestion to model serving in a production environment.

Should Have

• Experience with AI Agent integrations and generative AI-enabled data quality workflows.
• Strong understanding of scalable MLOps architecture and production ML systems.
• Experience in automated remediation of data quality issues.
• Ability to work independently in a hands-on technical SME role.
• Strong collaboration and cross-functional communication skills.

Skills

• MLOps.
• Google Cloud Platform (GCP).
• Vertex AI.
• Kubeflow.
• Vertex AI Pipelines.
• Kubeflow Pipelines.
• Cloud Storage.
• Artifact Registry.
• Docker.
• CI/CD.
• Machine learning pipelines.
• Data ingestion.
• Data transformation.
• Data validation.
• Feature engineering.
• Model training.
• Continuous training (CT).
• Hyperparameter tuning.
• Experiment tracking.
• Model versioning.
• Model deployment.
• Vertex AI Endpoints.
• Real-time inference.
• TensorFlow.
• Gemini API / Generative Language API.
• AI Agents.

Qualification and Education

• Bachelor’s degree in Computer Science, Data Science, Engineering, Information Technology, or a related field preferred.