MLOps Engineer (Automotive)

TL;DR

MLOps Engineer (Automotive ML): Design, implement, and maintain pipelines for transitioning machine learning models from research to production for car data analysis with an accent on scalable deployment in-cloud or in-vehicle, security, and low latency. Focus on monitoring systems, model performance comparison, validation automation, and collaboration with data scientists.

Location: Darmstadt, Remote

Company

hirify.global develops Virtual Sensors for Mobility to optimize vehicle design, usage, and maintenance, partnering with automotive manufacturers and Tier1 companies worldwide.

What you will do

• Design and maintain scalable pipelines for deploying ML models in-cloud or in-vehicle with minimal latency and secure integration.
• Implement monitoring systems to track model performance and detect issues.
• Develop methods and automate processes for model comparison, validation, accuracy, and consistency in production.
• Collaborate with data scientists, developers, and stakeholders to deliver tailored MLOps solutions.
• Create documentation for processes, pipelines, and workflows; provide training on MLOps best practices.

Requirements

• Fluent in written and spoken English; German is a plus.
• At least 2 years in modern DevOps practices and microservice architecture.
• Expertise in Kubernetes and containerization technologies.
• Hands-on experience with KubeFlow, Kserve, or equivalent; ML experimentation platforms like W&B or MLflow.
• Understanding of time series modeling and ML/NN frameworks.
• Strong collaboration and communication skills with cross-functional teams.

Nice to have

• Familiarity with AWS or other cloud providers.

Culture & Benefits

• Diverse international team of 35+ members from 10 nations.
• Focus on innovation in software-defined vehicles and sustainable mobility.
• Full-time permanent employment.