Senior Machine Learning Engineer

Essential functions

• Deploy, monitor, and manage ML models using KServe on Kubernetes.

• Build and maintain Kubernetes clusters (GKE) for scalable model serving.

• Develop, automate, and maintain ML workflows using Kubeflow Pipelines.

• Optimize performance and resource usage of deployed models.

• Collaborate with data scientists to productionize ML models.

• Set up monitoring and alerting for model health and latency.

• Ensure security, compliance, and reliability of ML infrastructure.

• Troubleshoot issues across the ML serving stack.

• Support continuous integration and deployment (CI/CD) for ML workflows.

• Document architecture, processes, and best practices.

Qualifications

• Hands-on experience with KServe, Kubernetes, and Docker.

• Strong proficiency in Java and Spring Boot.

• Experience with GCP (especially GKE) and Kubeflow Pipelines.

• Understanding of model performance management and real-time systems.

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.

Would be a plus

• GCP and/or Kubernetes certifications.

• Experience with ML observability or monitoring tools.

• Familiarity with deep learning models or recommendation systems.

• Exposure to data versioning and ML metadata tracking tools (e.g., MLflow, DVC).

• Experience optimizing inference performance for large-scale systems.

• Contributions to open-source MLOps tools or platforms.

We offer

• Opportunity to work on cutting-edge projects
• Work with a highly motivated and dedicated team
• Competitive salary
• Flexible schedule
• Benefits package - medical insurance, vision, dental, etc.
• 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.