3 дня назад
MLOps Engineering Lead (AI)
100 000 - 150 000€
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 Engineering Lead (AI) (Python, Azure ML): Building and operating production ML infrastructure that converts sensor data into clinical inference results, with an accent on reliable inference workflows, reusable MLOps components, and model lifecycle management. Focus on designing orchestration and distributed data pipelines, optimizing PyTorch serving at scale, and maintaining SLA-bound systems in a regulated medtech environment.
Location: Hybrid in London or Stockholm, with engineers typically working from the office 1–2 days a week
Salary: €100K–€150K plus equity
Company
develops preventive healthcare technology that combines non-invasive scanning, proprietary sensors, and clinical care to provide personalized health insights and support early detection.
What you will do
- Own the ML infrastructure that transforms sensor data into clinical inference results across skin, cardio, and future health domains.
- Build a reusable MLOps component library for Data Science and ML Engineering teams.
- Own, monitor, and operate business-critical inference workflows within defined SLAs.
- Embed experiment tracking, model lifecycle, and evaluation standards into Data Science Platform workflows.
- Right-size, track costs, and optimize Databricks and Azure ML infrastructure.
- Teach MLOps practices and support engineers without prior MLOps experience.
Requirements
- Production-grade Python experience building and owning end-to-end ML systems.
- Hands-on ownership of ML orchestration with Dagster or an equivalent platform.
- Production experience with Kubernetes, containerized workloads, and Terraform or equivalent infrastructure-as-code tooling.
- Experience designing distributed systems and data pipelines across multiple data sources or domains.
- Experience with MLflow or an equivalent solution for experiment tracking and model lifecycle management.
- Experience with PyTorch model serving, inference optimization, and integrated Azure ML, AKS, and Blob Storage platforms.
Nice to have
- Experience in regulated environments such as medical devices or ISO 13485.
- Exposure to computer vision or sensor data pipelines involving skin, cardio, LiDAR, or DICOM.
- Experience onboarding non-MLOps engineers to platform tooling.
Culture & Benefits
- Work in small, cross-functional engineering groups aligned to specific goals.
- Operate with team autonomy and collaborate across engineering and science disciplines.
- Participate in bi-weekly planning, progress reviews, and engineering demos.
- Flexible hybrid workplace designed to support work-life balance.
- Join an engineering organization spanning hardware, firmware, electrical design, algorithms, machine learning, optronics, and frontend development.
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