Grid Dynamics

Senior MLOps Engineer

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SeniorHybryda·WarszawaFull-time·B2B

#457169·Dodano 18 dni temu·4

Źródło: justjoin.it

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Tech Stack / Keywords

PythonMLOpsMLflow Terraform

Podsumowanie oferty

As a Senior MLOps Engineer on the Physical AI & Robotics Platform team, you will build and operate the ML platform that powers inference across a fleet of production robots. The role involves ensuring reliable, efficient, and scalable model deployment across cloud and edge environments, working closely with ML researchers, robotics engineers, computer vision experts, and data scientists. You will be responsible for bridging the gap between model training and production deployment and managing the ML lifecycle infrastructure.

Wymagania

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.

Obowiązki

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.

Benefity

Opportunity to work on cutting-edge projects
Highly motivated and dedicated team
Competitive salary
Flexible schedule
Benefits package including medical insurance and sports
Corporate social events
Professional development opportunities
Well-equipped office

Elastyczne godziny

Opieka zdrowotna

Karta sportowa

Spotkania integracyjne

Dofinansowanie szkoleń

Inne informacje

INFORMATION ON PERSONAL DATA PROCESSING by Grid Dynamics Poland sp. z o.o. and associated entities as co-administrators includes contacts, legal basis, purposes, recipients, data transfers outside EEA, processing duration, rights of data subjects, and complaint procedures as detailed in the offer.

Grid Dynamics

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