MLOps Engineer – Robotics ML Platform

Grid Dynamics is building its own Physical AI and robotics platform that helps manufacturers automate high-variability tasks that conventional, hand-programmed robots can't handle, by putting AI models directly in charge of how robots perceive and act. The platform already runs inference across a fleet of production robots, and models need to move reliably from research into the field, both in the cloud and on the robots themselves.
We're looking for an MLOps Engineer to build and run the ML platform behind that: GPU-accelerated serving, training and fine-tuning pipelines, and the release process that ties it all together.

Essential functions

1. Build and maintain the ML platform that runs inference pipelines on a fleet of production robots.
2. Deploy and optimise models on GPUs, making inference faster and more cost-efficient.
3. Own the release process end to end, from infrastructure as code and CI/CD pipelines to production deployments.
4. Develop and run training and fine-tuning pipelines on cloud infrastructure.
5. Manage datasets, model versions, and experiments with DVC and MLflow.
6. Build tools and libraries that let data scientists and robotics engineers ship models without dealing with infrastructure.
7. Keep cloud costs under control across AWS and GCP using spot instances and autoscaling.
8. Work with ML researchers and robotics engineers to take models from prototype to production.

Qualifications

Must-have

1. Hands-on experience taking ML models from training to production and keeping them running reliably.
2. Strong Python, plus practical experience with Docker, Terraform, and GitHub Actions.
3. Has served models on GPUs with NVIDIA Triton Inference Server and tuned them for latency, throughput, or cost.
4. Has worked with ML lifecycle tooling: MLflow for model registry, DVC for dataset versioning, and Kubeflow Pipelines.
5. Production experience on AWS and/or GCP, including spot/preemptible instances and GPU autoscaling.
6. Has built CI/CD and infrastructure as code for ML workloads, including automated testing of models and pipelines.

Would be a plus

1. Has run fine-tuning or training pipelines on cloud infrastructure.
2. Exposure to edge deployment (for example, NVIDIA Jetson) or robotics systems.
3. Experience building internal tools or libraries used by data scientists or researchers.

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.