Machine Learning Engineer (m/f/x)

Responsibilities

We are representing a confidential B2B technology platform in Berlin that is scaling its machine learning capability from four production models today to fifteen to twenty by mid-2027. You will own everything that happens after model handoff: packaging, deployment, drift detection, performance monitoring, and the final call on whether a model is fit to serve real traffic.

Your core responsibilities include:

• Taking ownership of models from development handoff through to production deployment, including packaging, serving infrastructure, monitoring, and go-live decisions
• Maintaining reliability of production models through drift detection, performance monitoring, alerting, and incident response when behaviour changes
• Managing the full serving and inference path: fitted pipeline artifacts, inference entry points, monitoring hooks, and feature-store parity
• Reviewing model design and evaluation methodology before release, catching data leakage, backward-window errors, and weak evaluation strategies while they are still cheap to fix
• Extending a shared, canonical ML platform so it remains useful for every model without project-specific logic leaking into shared code
• Setting and maintaining the engineering standards the platform runs on as it scales across the organisation

What we're looking for

Our client operates a unified ML platform built on Snowflake, SageMaker, and Terraform-managed AWS infrastructure. You will work within a governed, five-stage pipeline covering data extraction, validation, transformation, training, and evaluation. This is not a role for someone who only trains models — you need real production ownership experience.

Must-have skills:

• 2+ years in production machine learning engineering, with demonstrated ownership of models after handoff
• Strong Python: typed, tested, production-grade code, with experience reviewing the work of others
• Sufficient ML depth to challenge pipelines on problem framing, feature engineering, model selection, and evaluation methodology
• Hands-on experience with a managed ML platform such as SageMaker, Vertex AI, Databricks, or Azure ML
• Practical experience with feature stores, CI/CD for machine learning, AWS, and Terraform
• Active use of AI tools such as Claude, ChatGPT, or Copilot in daily work
• English at C1 level, written and spoken; German is not required

Nice to have:

• Snowflake and dbt experience
• Experience mentoring colleagues or conducting technical reviews
• Comfort working in ambiguous or undefined problem spaces

Location and working model: Berlin Schöneberg, hybrid with 3 days in office and 2 days remote, plus 25 "Work from Anywhere" days per year.

How we work with you

Candidates apply directly through Get A Job.ai. Our talent team will screen your application and arrange an initial conversation. If there is mutual interest, we will submit your profile to our client and coordinate the interview process on your behalf. Please do not contact the client directly — all communication flows through Get A Job.ai to protect your candidacy and ensure a structured process.

Pay

Pay details will be discussed during the screening process and are competitive for senior ML engineering roles in Berlin.

Get A Job.ai is an equal opportunity recruiter. We welcome applications from candidates of all backgrounds and do not discriminate on the basis of race, religion, gender, sexual orientation, age, disability, or any other protected characteristic.