ML Engineer with SageMaker

We are looking for an ML engineer to join the team and realize the complete ML lifecycle in our AWS environment. Deploy models into SageMaker using Apache Airflow

Essential functions

• Batch Inference Management
• Model Deployment and Lifecycle Management in SageMaker
• Custom Docker Container Management
• MLOps and CI/CD Integration
• Model Tracking and Monitoring

Qualifications

Amazon SageMaker:

• Experience with Batch Transform for large-scale batch inference.
• Deploying, training, and managing models in SageMaker, including handling model artifacts and versioning.
• Knowledge of Docker and SageMaker’s ability to run custom Docker containers for executing models.
• Knowledge of tracking and model management as MLOps best practices for integrating batch predictions into continuous delivery and integration workflows.

Model Lifecycle:

• Model monitoring, model drifting, data drifting,
• Automate model retraining and tunning
• Comfortable Experiment tracking technologies (eg. MLFlow)

We offer

• Opportunity to work on bleeding-edge projects
• Work with a highly motivated and dedicated team
• Competitive salary
• Flexible schedule
• Benefits package - medical insurance, sports
• Corporate social events
• Professional development opportunities
• Well-equipped office

About us

Grid Dynamics (NASDAQ: GDYN) is a leading provider of technology consulting, platform and product engineering, AI, and advanced analytics services. Fusing technical vision with business acumen, we solve the most pressing technical challenges and enable positive business outcomes for enterprise companies undergoing business transformation. A key differentiator for Grid Dynamics is our 8 years of experience and leadership in enterprise AI, supported by profound expertise and ongoing investment in data, analytics, cloud & DevOps, application modernization and customer experience. Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the Americas, Europe, and India.