Senior Data Engineer- ML & AI Platform

At Marktplaats, data is at the heart of everything we do, but Intelligence is what differentiates us. We are building the ML/AI Platform that powers innovation across our entire Product & Technology landscape.

You will join the Data Platform team
—the engineering engine behind our Data & Analytics crew. You are stepping into a unique,
hybrid ecosystem
: we maintain a robust, high-scale traditional ML environment used daily by teams across Marktplaats, while simultaneously acting as the architects for our emerging GenAI capabilities
. We are a team that values engineering rigor just as much as experimentation, looking for a leader to help us bridge the gap between stable production services and the bleeding edge of AI.

As a Senior Staff Data Engineer - ML & AI Platform
, you will be the bridge between infrastructure and Data Science
, ensuring that our ML/AI environment is robust, scalable, and developer-friendly
. Your mission is to solve the "last mile" problem of ML: making it easy to move models from a notebook to a high-scale production API.

You are an R&D-minded leader who loves to experiment with the latest features (e.g.,
Databricks Agents
, RAG Studio
) and operationalize them into stable platform features. You will empower AI/ML Engineers and Data Scientists to be autonomous by building the tooling they need to self-serve
.

• Platform Leadership: Lead the evolution of our Machine Learning & AI Platform, designing the architecture for AI Agents and establishing patterns for Vector Databases
  .

• Operationalize Innovation: Act as a "first mover." When Databricks releases a new feature (e.g.,
  Liquid Clustering
  , Agent Evaluation
  ), you validate it and integrate it into the platform.

• GenAI Governance: Write the guidelines for GenAI development
  , helping teams transition from "notebook experiments" to production-grade LLM applications
  .

• Enablement: Design the Feature Store
  , manage the Model Registry
  , and set up the infrastructure for Vector Search and RAG (Retrieval Augmented Generation) workflows.

• Mentorship: Elevate the technical bar of the team, mentoring Staff and Senior engineers on design patterns
  , code quality, and architectural decisions.

• Translation: Translate complex requirements from ML Engineers and Data Scientists into robust engineering tickets and infrastructure roadmaps
  .

• Databricks AI Stack: MLflow, Mosaic AI, Unity Catalog, Feature Store, Databricks Model Serving, Vector Databases.

• Big Data & Compute: Apache Spark (Internals & Optimization), AWS (GPU instances, EC2).

• GenAI & Agents: Databricks Agent tools.

• Languages: Python (Expert level), PyTorch.

• Infrastructure as Code: Terraform, Terragrunt.

• Containerization: Docker, Kubernetes.

• CI/CD: Git Hub Actions.

• Observability: Datadog.

• Experience: 10+ years of experience with a specific focus on the intersection of Data Engineering
  , MLOps
  , and AI Infrastructure
  .

• Spark Mastery: You don't just run jobs; you optimize them. You possess deep knowledge of Spark internals
  , structured streaming
  , and performance tuning for large-scale data processing.

• MLOps Authority: Proven experience architecting end-to-end ML platforms for Traditional ML (Classic MLOps) while actively enabling the organization on Generative AI concepts.

• Dev Ops Mindset: You treat infrastructure as software
  . You have a strong background in building automated pipelines and ensuring system observability.

• GenAI Readiness: Practical experience building infrastructure for Large Language Models
  , including managing the complexity of chaining models and tools.

• Serving Expertise: Solid experience serving models at low latency and high concurrency using containerized solutions.

• Collaborative Spirit: You speak the language of AI/ML Engineers and can effectively bridge the gap between "experimental code" and "production systems".