Senior/Staff Machine Learning Engineer

About This Opportunity

We are representing a confidential e-commerce organization in their search for a Senior/Staff Machine Learning Engineer based in Berlin. This is a rare greenfield opportunity to build MLOps infrastructure from the ground up and drive AI initiatives across authentication, search, recommendations, and broader marketplace capabilities.

Our client is a global platform operating in the sustainable fashion space, and this role will serve as a cornerstone hire to establish ML engineering discipline across the organization. You'll bridge applied science, data platform, and backend engineering teams while designing robust, scalable architectures that deliver high-throughput, low-latency business impact.

Responsibilities

Short-Term Impact (First 6 Months):

• Partner with operations teams and data scientists to transform ML and RAG prototypes into production-ready systems
• Deploy, optimize, and scale computer vision and VLM models for fraud detection and product authentication
• Directly integrate with teams to improve trust and safety infrastructure

Mid-Term Foundation (MLOps Lifecycle):

• Design end-to-end ML lifecycle systems including data management, feature stores, model tracking, registry, serving, and monitoring
• Automate continuous retraining pipelines handling diverse deployment cadences (daily fraud detection to weekly recommendations)
• Build resilient multi-model architectures and evaluate technical overhead of in-house tools versus enterprise platforms
• Ensure long-term system maintainability and engineering hygiene

Long-Term Vision (Centralized ML Capabilities):

• Act as a pioneer hire establishing technical standards for the AI/ML organization
• Transition to a centralized foundational role providing horizontal ML infrastructure support
• Mentor teams and scale infrastructure across search, discovery, pricing, marketing, and data platforms
• Set engineering best practices for the growing ML discipline

What We're Looking For

Must-Have Qualifications:

• 5-8+ years of hands-on Machine Learning Engineering experience building and scaling MLOps infrastructure and productionizing ML systems
• Proven expertise deploying low-latency, high-throughput ML inference services using FastAPI, TorchServe, Triton Inference Server, or Ray Serve
• Experience with both classical lightweight and heavy-width ML models in PyTorch/TensorFlow
• Strong preference for AWS ecosystem (EKS, EC2, SageMaker) and Snowflake, along with open source tooling
• Deep experience building automated continuous model retraining pipelines to handle concept drift with daily to weekly cycles
• Expertise orchestrating decoupled, multi-model architectures using Airflow, Kubeflow, or Metaflow
• Strong proficiency with model registry and tracking tools like MLflow or Weights & Biases
• Hands-on experience with online (Redis, DynamoDB) and offline (Snowflake, S3) Feature Stores in production environments
• Familiarity with Feast or custom dbt-based pipelines highly valued
• Strategic builder mindset: ability to evaluate TCO for internal systems versus enterprise tools and design for scalability
• Excellent cross-functional communication skills and ability to translate complex ML prototypes into production code
• Strong version control, rigorous testing, and CI/CD best practices

Nice-to-Have Experience:

• Background in e-commerce, marketplaces, search & recommendation, trust & safety, or fraud detection
• Hands-on experience with vector databases, visual RAG pipelines, and deploying deep learning VLM models
• Model optimization for edge computing or low-latency inference (ONNX, TensorRT)
• Advanced experience with Docker, Kubernetes, Infrastructure as Code (Terraform), and dbt workflows
• Expertise setting up monitoring for model performance, concept drift, and system health using Datadog or Prometheus

How We Work With You

When you apply through Get A Job.ai, here's what happens:

• Our talent team reviews your application and conducts an initial screening to understand your background and career goals
• We prepare and submit qualified candidates directly to our client with a strong recommendation
• We coordinate the interview process and provide guidance throughout
• Please do not contact the client directly—all communication flows through our recruiting team to ensure the best experience

We believe talent comes in many forms. If you don't meet every single requirement but have strong relevant experience, we encourage you to apply. Your unique perspective could be exactly what this team needs.

Pay

Compensation details will be discussed during the screening process and are competitive for the Berlin market at the Senior/Staff level.

Equal Opportunity: Get A Job.ai is committed to inclusive hiring practices. We welcome applications from candidates of all backgrounds and work to ensure fair consideration throughout our process.