(Senior) Machine Learning Engineer – Pricing (m/f/d)

Auxmoney GmbH is seeking a skilled Machine Learning Engineer to join their dynamic Pricing team. This is an exciting opportunity for individuals who are passionate about moving machine learning solutions beyond the experimental phase and into robust, production-ready systems that deliver tangible business value. You will play a pivotal role in shaping the future of auxmoney’s pricing strategies by building and maintaining the infrastructure that powers their dynamic pricing platform.

About the Role

At the heart of this position lies the operationalisation of machine learning. You will be instrumental in ensuring that ML models are not only developed but also reliably deployed, monitored, and maintained in live environments. This involves taking end-to-end ownership, from the initial stages of model development and integration right through to deployment and ongoing performance monitoring. You will work at the crucial intersection of machine learning engineering and the intricacies of pricing, contributing directly to a core business function.

A significant aspect of your work will involve collaborating closely with cross-functional teams, including Data Science, Data Infrastructure, and various business stakeholders. Your ability to translate complex business and analytical requirements into practical, technical solutions will be key. You'll essentially act as a vital bridge, ensuring seamless communication and effective collaboration between these diverse groups and contributing to the technical blueprint of auxmoney's pricing capabilities.

The role also offers the chance to engage with modern data platforms. You will support and implement training workflows within Snowflake-based environments, ensuring that both model development processes and data platform components operate smoothly throughout the entire machine learning lifecycle. Furthermore, you'll be involved in building and enhancing applications and services that surround the company’s Dynamic Pricing System, thereby enabling sophisticated ML-driven logic within pricing workflows. A systems-thinking approach will be essential, as you'll need to maintain a clear understanding of complex system landscapes, identify dependencies and interfaces, and robustly integrate ML components into existing technological ecosystems.

Your responsibilities will encompass a broad spectrum of machine learning engineering tasks, with a strong emphasis on production readiness and operational excellence:

• **End-to-End Model Ownership:** You will take full accountability for machine learning models, guiding them from conception and development through to integration, deployment, and continuous monitoring in live production systems.
• **Reliable Operationalisation:** Focus on making machine learning models stable and maintainable, proactively identifying and mitigating common challenges encountered when moving models from experimental setups to production environments.
• **Snowflake Environment Enhancement:** Contribute to and implement training workflows within Snowflake, optimising the synergy between model development activities and data platform components across the ML lifecycle.
• **Platform Development:** Build and further develop crucial applications and services that form the backbone of the Dynamic Pricing System on Snowflake, empowering ML-driven decision-making in pricing.
• **Systems Integration:** Understand and navigate intricate system landscapes, including applications, services, data flows, and infrastructure, ensuring that ML components are seamlessly and reliably integrated.
• **Cross-Functional Liaison:** Effectively translate business and analytical requirements into tangible technical solutions, fostering strong working relationships between stakeholders, Data Science, and Data Infrastructure teams.

What They're Looking For

Auxmoney is searching for an experienced professional with a solid foundation in machine learning operations and software engineering principles:

• **Proven MLOps Experience:** Demonstrable hands-on experience in successfully bringing machine learning models into production and operating them reliably in live systems is essential.
• **Production-Ready Python Skills:** The ability to write stable, production-oriented Python code is a core requirement, alongside a strong grasp of software architecture and testing methodologies, including the use of scikit-learn pipelines.
• **End-to-End ML Lifecycle Familiarity:** Experience spanning the entire machine learning lifecycle, from training and feature engineering to evaluation, monitoring, drift detection, addressing training-serving skew, and managing model versions.
• **Data Platform Expertise:** Prior experience working with Snowflake or similar data platforms for data-intensive and machine learning-related use cases.
• **System Integration Acumen:** The capability to navigate and integrate ML components within complex application, service, data flow, and infrastructure landscapes.
• **Strong Collaboration Skills:** Excellent communication abilities, enabling effective interaction with both technical and non-technical stakeholders and fostering productive teamwork across different domains.
• **Ownership Mindset:** A self-driven, structured, and reliable approach to work, taking initiative and responsibility from beginning to end.
• **DevOps Fundamentals:** Familiarity with CI/CD pipelines and Docker is expected.
• **Language Proficiency:** Confident professional communication in English is required.

While not strictly mandatory, experience within the pricing domain, such as dynamic pricing, price optimisation, or pricing experimentation, would be considered a valuable asset.

This role offers a flexible working arrangement, with the option for remote work within Germany or a hybrid model at their Düsseldorf or Berlin offices, depending on personal preference and team alignment. Auxmoney is an equal opportunity employer, actively encouraging applications from diverse backgrounds.

In South Africa, a Machine Learning Engineer role, particularly one with a senior component and productionisation focus, can command competitive salaries, often ranging from R600,000 to over R1,000,000 annually, depending heavily on experience, the specific industry, and the complexity of the projects. Employers typically include fintech companies, large e-commerce platforms, and established tech firms. The career path often involves progression to Lead ML Engineer, Architect roles, or specialised AI/ML leadership positions.

• MLOps experience: You have strong hands-on experience productionizing machine learning models and operating them reliably in productive systems.
• Python engineering: You write stable, production-oriented Python code and are confident with software architecture and testing, including scikit-learn pipelines.
• End-to-end ML lifecycle: You have experience across training, feature engineering, evaluation, monitoring, drift detection, training-serving skew, and model versioning.
• Data platforms: You have worked with Snowflake or comparable platforms in data-intensive and ML-related use cases.
• System integration: You can navigate complex landscapes across applications, services, data flows, and infrastructure, and you integrate ML components thoughtfully.
• Collaboration skills: You communicate clearly with technical and non-technical stakeholders and work effectively across teams and domains.
• Ownership mindset: You work in a structured, self-driven, and reliable way, and you take ownership from start to finish.
• DevOps basics: You are familiar with CI/CD pipelines and Docker.
• Nice to have: You bring pricing domain knowledge, for example dynamic pricing, price optimization, or pricing experimentation.
• Fluent English: You communicate confidently in English in a professional setting.