Ai/ML Software Development SME

Job Overview:

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.

Company Overview:

Founded in 2010 and headquartered in the Washington, DC metro area, Cynet Systems Inc. is a leading staffing and recruiting powerhouse. Proudly recognized as a nationally and locally certified diversity firm, Cynet delivers agile, scalable talent solutions across industries. With an active footprint in all 50 U.S. states and Canada, we support thousands of consultants through our expansive, high-performing recruitment engine operating across North America and Asia—ensuring speed, quality, and consistency in every hire.