3 дня назад
MLOps Engineer (AI)
Вакансия из Hirify Global, списка международных tech-компанийДля мэтча и отклика нужен Plus
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
MLOps Engineer (AI) (Python/ML platform): Building and scaling Gamma Ulixes for training, evaluating, and deploying machine learning models with an accent on reproducible workflows, GPU infrastructure, and production-grade AI services. Focus on designing automated training pipelines, deploying inference across cloud, on-premise, and edge environments, and optimizing models for Nvidia Jetson and embedded hardware.
Location: Paris, France; on-site
Company
is an air defense company developing AI-guided interceptors for drones and cruise missiles, together with the software platform that powers them. Its customers include NATO-aligned militaries and defense institutions.
What you will do
- Develop Gamma Ulixes, an AI platform for training, evaluating, and deploying machine learning models.
- Build experiment tracking, model registry, dataset management, benchmarking, evaluation, and model validation services.
- Design reproducible ML workflows, automated training pipelines, APIs, backend services, and scalable infrastructure for computer vision and trajectory-based machine learning.
- Manage GPU infrastructure, distributed training, resource orchestration, monitoring, observability, and reliability across AI services.
- Build CI/CD pipelines for ML models and deploy AI services across cloud, on-premise, and edge environments.
- Collaborate with Machine Learning, Software, and Infrastructure teams to industrialize research workflows and define engineering standards.
Requirements
- 3–5+ years of experience as an MLOps Engineer, ML Platform Engineer, Software Engineer, or Data Engineer working on production machine learning systems.
- Strong Python software engineering skills.
- Experience with production-grade ML infrastructure, experiment tracking, model registries, and reproducible workflows.
- Strong understanding of Docker, Linux, and containerized environments.
- Experience designing APIs and backend services for AI applications.
- Familiarity with CI/CD, infrastructure automation, cloud-native development, and cross-functional technical ownership.
Nice to have
- Experience with computer vision, trajectory-based machine learning, distributed training, or Nvidia Jetson and embedded hardware.
Culture & Benefits
- Build products and developer tooling, not only infrastructure.
- Focus on developer experience, operational excellence, reliability, and maintainability.
- Work in a fast-moving environment with high technical standards and complex ownership areas.
- Contribute to engineering standards, documentation, and continuous improvement across AI, Software, and Infrastructure teams.
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