Machine learning engineer Jobs in Dinslaken

Salary: 80.000 - 100.000 per year

Requirements:

• Experience: 5+ years in Software Engineering, with at least 3 years focused on DevOps, Release Engineering, or Infrastructure.
• Architectural Mindset: Proven track record of designing and implementing CI/CD workflows from scratch, demonstrating the ability to think strategically about build systems.
• Tech Stack Proficiency: Strong experience with Java programming (Spring/JEE), ecosystems and build tools (Maven/Gradle plugin development).
• Containerization & Orchestration: Deep understanding of Docker and container orchestration (e.g. Kubernetes/OpenShift, Terraform, Ansible).
• CI/CD Tools: Proficiency with Jenkins or alternatives (creating pipelines, managing plugins).
• Artifact Management: Experience administering Nexus or Artifactory.
• GitHub Workflow: Solid workflow knowledge of GitHub (branching strategies, Actions, Hooks).
• Scripting: Competence in scripting (Bash, Python, or Groovy) for automation tasks.
• Operating Systems: Strong working knowledge of Linux environments.
• Education: Bachelors degree in Computer Science, Engineering, or equivalent practical experience.
• Preferred Qualifications: Experience supporting Eclipse IDE setups for development teams; background in the financial sector, specifically with algorithmic trading or futures markets; knowledge in secure virtual networking, cloud/grid-computing, failover/load-balancing strategies and high-availability concepts.

Responsibilities:

• CI/CD Architecture & Ownership: Take full responsibility for the architectural design and operational implementation of the continuous integration and delivery processes.
• Pipeline Implementation: Build and manage complex pipelines to ensure automated testing and reliable deployment of Java-based trading algorithms, including scaling Jenkins or migrating to alternatives like GitHub-Actions.
• Infrastructure & Containerization: Manage application containerization using Docker to maintain consistency across development, testing, and production environments.
• Artifact Management: Maintain and organize the Sonatype Nexus repository for efficient dependency management and build artifact storage.
• Collaboration & Standardization: Work closely with the development team to support the Eclipse-based development environment, streamline GitHub workflows, and enforce best practices for version control and release management.
• System Reliability: Monitor and ensure the health and performance of the trading infrastructure, guaranteeing high availability for futures trading.
• Automation: Automate configuration management and repetitive operational tasks to enhance team productivity.
• Troubleshooting: Diagnose and resolve issues related to build, deployment, and runtime environments.

Technologies:

• Ansible
• Bash
• CI/CD
• Cloud
• DevOps
• Docker
• Eclipse
• GitHub
• Gradle
• Groovy
• Support
• Java
• Jenkins
• Kubernetes
• Linux
• Machine Learning
• Maven
• Nexus
• OpenShift
• Python
• Spring
• Terraform
• Java EE

More:

We develop technological solutions for algorithmic trading and quantitative investing.
Our mission is to merge data-driven research, advanced software engineering, and a deep understanding of global markets to build trading systems that analyze markets systematically, execute strategies automatically, and manage risk with precision.

At the core of our approach lies a belief that performance in financial markets can be achieved through logic, mathematics, and technology not speculation. Our proprietary systems are designed to identify repeatable market patterns, optimize execution, and adapt to changing conditions in real time.

By combining quantitative models, machine learning, and high-performance computing, we transform complex data into actionable insights. Our teams operate at the intersection of FinTech, research, and applied mathematics, bridging the gap between academic rigor and practical implementation.

We continuously refine our strategies through extensive backtesting, real-time validation, and iterative optimization. This ensures that every line of code and every model we deploy contributes directly to consistent, measurable performance independent of market cycles or volatility.

Investors and partners choose us because we make complex markets understandable, automate what others still do manually, and translate innovation into tangible results.

Our culture is built on precision, transparency, and intellectual curiosity. We believe in systematic thinking, open collaboration, and a relentless pursuit of improvement because in the world of quantitative finance, every detail counts.

last updated 18 week of 2026