Machine Learning Engineer

• Design, build, and maintain MLOps infrastructure, establishing ML CI/CD best practices including model testing, versioning, and deployment.
• Develop and manage scalable automated pipelines for training, evaluating, and deploying ML models, with emphasis on LLM systems.
• Implement robust monitoring and logging for production models to track performance, drift, and data quality.
• Collaborate with Data Scientists to containerize and productionize models and algorithms, including RAG and Graph RAG approaches.
• Manage and optimize cloud infrastructure for ML workloads (e.g., Amazon Bedrock), focusing on performance, cost-effectiveness, and scalability.
• Automate provisioning of ML infrastructure using Infrastructure as Code.
• Integrate ML models into production product architecture, working closely with product and engineering teams.
• Own operational aspects of AI lifecycle: deployment, A/B testing, incident response, and continuous improvement.
• Contribute to AI strategy and roadmap, advising on operational feasibility and scalability of AI features.
• Collaborate with Principal Data Scientists and Principal Engineers to ensure MLOps framework supports full AI workflows and model interaction layers.

Machine Learning Operations Engineer, AI

Senior Machine Learning Scientist

Senior Manager – Principal AI Engineer

Staff ML Engineer – R&D

Lead Engineer, MLOps

Founding Engineer