Senior MLOps Engineer

Grid Dynamics (NASDAQ: GDYN) is a leading provider of technology consulting, platform and product engineering, AI, and advanced analytics services. Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices in the Americas, Europe, and India. The company specializes in enterprise AI, data, analytics, cloud & DevOps, application modernization, and customer experience.

• Solid hands-on experience in MLOps taking machine learning models from training to production deployment.
• Proficiency in Python, with working knowledge of Docker, Terraform, and GitHub Actions.
• Experience serving and optimizing ML models on GPUs using NVIDIA Triton.
• Familiarity with ML lifecycle and versioning tools such as MLflow, DVC, and Kubeflow Pipelines.
• Hands-on experience with cloud platforms AWS and/or GCP, including spot/preemptible instances and GPU autoscaling.
• Understanding of CI/CD pipelines, Infrastructure as Code (IaC), and automated testing for ML workloads.
• Experience with model fine-tuning techniques and running distributed training pipelines on cloud infrastructure.

Nice to have:

• Experience with edge deployments or robotics environments.
• Experience deploying computer vision models or working within the Physical AI domain.

• Build and maintain the ML platform powering inference pipelines across production robots.
• Deploy and optimize ML models on GPUs using NVIDIA Triton for fast and cost-efficient inference.
• Own end-to-end release lifecycle including Infrastructure as Code using Terraform and CI/CD pipelines with GitHub Actions.
• Develop and execute scalable training and fine-tuning pipelines on cloud infrastructure across AWS and GCP.
• Manage datasets, model registries, and experiment tracking with tools like DVC, MLflow, and Kubeflow Pipelines.
• Control cloud infrastructure costs using spot/preemptible instances and GPU autoscaling.
• Build developer tooling and internal libraries to facilitate seamless model deployment.
• Collaborate cross-functionally with ML researchers, computer vision specialists, and robotics engineers to transition models to production.

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