Machine Learning Engineer - AI Core

Omnitracs; Remote; EUR 70.000 - 90.000; Vollzeit

Zusammenfassung

A leading tech firm in Germany seeks a Machine Learning Engineer to leverage AI for vehicle claims optimization. The role involves designing computer vision models, building scalable ML pipelines, and deploying services using Docker and Kubernetes. Ideal candidates will possess strong Python skills, experience with TensorFlow or PyTorch, and familiarity with GCP tools. This position offers autonomy and a production-driven work environment.

Qualifikationen

• Strong Python skills and software engineering fundamentals.
• Proven experience in training and deploying CV models using TensorFlow or PyTorch.
• Ability to design effective APIs and services.

Aufgaben

• Design, train, and ship computer vision models for vehicle damage detection.
• Build scalable data and ML pipelines on GCP.
• Deploy and operate services using Docker and Kubernetes.

Kenntnisse

Python

Computer Vision

TensorFlow

PyTorch

CI/CD

GCP

APIs Design

Kubernetes

Tools

Docker

Grafana

FastAPI

Streamlit

Jobbeschreibung

Machine Learning Engineer – AI Core

Mission

Leverage AI and Solera’s data assets to develop, deliver, operate, and maintain innovative, production-grade components that make vehicle claims and ownership simpler, faster, and more efficient for customers and users.

What you will do

• Design, train, and ship computer vision models for vehicle damage detection (classification, detection, segmentation), as well as tree-based models and LLM-powered components.
• Build scalable data and ML pipelines on GCP (BigQuery, Dataflow, Vertex AI) for training, evaluation, and inference at scale across hundreds of millions of images and claims.
• Deploy and operate services on GKE/Cloud Run with Docker and Kubernetes, following CI/CD with robust build systems and testing.
• Expose models via FastAPI; build internal tools and demos with Streamlit; instrument monitoring and alerting with Grafana.
• Own the end-to-end lifecycle: problem framing, data curation, experimentation, model/productization, performance/cost optimization, and post-deployment monitoring.
• Contribute to a high-quality monorepo: code reviews, standards, documentation, testing, and reproducibility.
• Collaborate in an internationally distributed team, driving clarity, sharing best practices, and improving ML/engineering workflows.

How we work

Monorepo with strong build, CI/CD, and code quality practices.

Freedom to choose the best tool for the job; high autonomy and ownership.

Production mindset: reliability, observability, maintainability, and measurable impact.

Tech stack

Python; TensorFlow, PyTorch

GCP: BigQuery, Dataflow, Vertex AI, GKE, Cloud Run, Cloud Deploy

Docker, Kubernetes

FastAPI, Streamlit

Grafana

What you bring

• Strong Python and software engineering fundamentals (testing, code quality, CI/CD, performance).
• Proven experience training and deploying CV models (classification, detection, segmentation) with TensorFlow/PyTorch.
• Proficiency with large-scale datasets and distributed processing on GCP (BigQuery, Dataflow) or similar.
• Production MLOps experience on Kubernetes/containers.
• Ability to design clean APIs and services (FastAPI) and build usable internal tools (Streamlit).
• Experience with tree-based models.
• Experience with integrating LLM APIs into production workflows.
• Structured problem solving, critical thinking, and a driven, ownership-oriented mindset.
• Effective communication and collaboration in a distributed, cross-functional environment.

Nice to have

• Vertex AI pipelines.
• GPU optimization and cost/performance tuning for training/inference.
• Experience in insurance, automotive, or related computer vision domains.