Senior MLOps Engineer

Essential functions

• ML Platform & Fleet Inference: Build and maintain the ML platform that powers inference pipelines and serves models reliably across a fleet of production robots.

• GPU Optimization & Serving: Deploy and optimize ML models on GPUs using NVIDIA Triton to achieve fast, low-latency, and cost-efficient inference.

• Release Process & IaC Ownership: Own the end-to-end release lifecycle for ML workloads, from Infrastructure as Code (Terraform) and CI/CD pipelines (GitHub Actions) to production deployments.

• Training & Fine-Tuning Pipelines: Develop and execute scalable training and fine-tuning pipelines on cloud infrastructure across AWS and GCP.

• Lifecycle & Dataset Management: Manage datasets, model registries, and experiment tracking using tools like DVC, MLflow, and Kubeflow Pipelines.

• Cloud Cost Optimization: Maintain strict control over cloud infrastructure costs across AWS and GCP by leveraging spot/preemptible instances and GPU autoscaling.

• Developer Tooling & Abstractions: Build tools and internal libraries that enable data scientists and robotics engineers to ship models seamlessly without dealing with raw infrastructure.

• Cross-Functional Collaboration: Partner closely with ML researchers, computer vision specialists, and robotics engineers to transition models from prototype to high-availability production environments.

Qualifications

• MLOps Experience: Solid foundation in MLOps, with hands-on experience taking machine learning models from training to production deployment.

• Core Engineering & Automation: Proficiency in Python, along with working knowledge of Docker, Terraform, and GitHub Actions.

• GPU Model Serving: Demonstrated experience serving and optimizing ML models on GPUs using NVIDIA Triton.

• ML Lifecycle Tooling: Familiarity with ML lifecycle and versioning tools, including MLflow for model registry, DVC for dataset versioning, and Kubeflow Pipelines.

• Cloud Infrastructure & Autoscaling: Hands-on experience with AWS and/or GCP, including managing spot/preemptible instances and GPU autoscaling.

• ML CI/CD & Testing: Clear understanding of CI/CD pipelines, Infrastructure as Code (IaC), and automated testing practices tailored for ML workloads.

• Training Pipelines: Familiarity with model fine-tuning techniques and running distributed training pipelines on cloud infrastructure.

Would be a plus

• Edge & Robotics Experience: Direct exposure to edge deployments or robotics environments.

• Computer Vision & Physical AI: Experience deploying CV models or working within Physical AI domain frameworks.

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.