Artificial Intelligence Engineer

Role & responsibilities

The Al Engineer, specializing in MLOps (Machine Learning Operations) and GenAl Ops

(Generative Al Operations), ensures that Al models, particularly machine learning and generative Al models, are efficiently operationalized and integrated into Customers workflows.

Responsibilities:

Model Deployment: Deploy machine learning and generative AI models into production environments using MLOps best practices, ensuring models are scalable, reliable, and easy to update.

Pipeline Automation: Automate end-to-end machine learning pipelines, from data ingestion and model training to deployment and monitoring.

Monitoring and Maintenance: Monitor AI models in production to ensure

optimal performance, handle model drift, and retrain models as necessary.

Infrastructure Management: Manage cloud and on-premises infrastructure to support the deployment and scaling of machine learning models.

Model Versioning: Implement model versioning and governance to track different versions of models and ensure traceability and reproducibility.

Collaboration with Data Scientists: Work closely with Data Scientists to operationalize their models, providing feedback on performance, scalability, and infrastructure needs.

Experiment Tracking: Use MLOps tools (e.g., MLflow, Kubeflow) to track experiments, ensuring that the best models are selected for production.

Generative AI Operations: For generative AI models, ensure proper deployment and integration into workflows, including tuning, prompt engineering, and model output optimization.

Performance Optimization: Continuously optimize /\I models and pipelines for performance, ensuring low latency, high throughput, and efficient resource use

Preferred candidate profile

Need to travel onsite often