ML Engineer - Amsterdam - 12 months contract

Global Enterprise Partners is currently looking for a ML Engineer to support our client in Amsterdam on a new project.

Key Responsibilities

. Cloud Development & Migration: Set up and guide the migration from on-premise infrastructure to GCP, including pipelines and Terraform-based infrastructure.
. Technical Leadership: Engage confidently with diverse stakeholders-data scientists, architects, and enterprise architects-to align technical decisions with business needs.
. ML Engineering Collaboration: Work closely with data scientists, understand their processes (eg, reinforcement learning), and translate these into scalable, cloud-native architectures.
. Infrastructure Ownership: Although the current on-prem infrastructure is managed externally (eg, Kubernetes platform), more responsibility is shifting to the team. The candidate will help define and manage this scope within GCP.
. Agile Adaptability: Operate effectively in the companies' fast-changing environment. The ability to handle delays or shifting priorities with resilience and professionalism is essential.

Required Skills & Qualities
. Hands-on experience with GCP, Terraform, and CI/CD pipelines.
. Ability to bridge technical and strategic discussions across multiple disciplines.
. Deep understanding of ML engineering workflows, including training pipelines and their architectural impact.
. Comfortable navigating dynamic environments and overlapping responsibilities.
. Capable of writing and reviewing basic Terraform code, and supporting data scientists in engineering tasks.

Team Dynamics
. The role emphasizes cross-functional collaboration, especially with data scientists who are expected to have foundational ML engineering skills.
. While roles differ in focus, the candidate must be able to communicate fluently across both engineering and data science domains.
Dual Stack Architecture & Transition to Google Cloud

The client currently operates with two parallel technology stacks:
 
1. On-Premise Stack
This stack includes:
. Kubernetes and Docker for container orchestration and deployment
. Apache Airflow for workflow orchestration
. MLflow for machine learning life cycle management
. ZOE (a Docker-based tool with a wrapper layer)
. GitHub for version control
. CI/CD pipelines using GitHub Actions
 
2. DCP Stack - Transitioning to Google Cloud
The client is actively migrating toward Google Cloud Platform (GCP), where GitHub will remain a central component. The cloud stack includes:
. Serverless compute via Cloud Run
. BigQuery for data warehousing and analytics
. Google Cloud Storage (GCS) for object storage
. Cloud Scheduler for task automation
. Terraform for infrastructure as code
. APIM hosted in Azure
 
Modeling & Machine Learning Landscape
The modelling approach is evolving and includes:
. PyTorch-like frameworks for deep learning
. Generative AI (GenAI) applications
. Decision Trees and Gradient Boosted Trees
. A growing focus on Reinforcement Learning (RL), which is expected to expand beyond the current single model
. Development of Recommendation Systems based on RL principles

Imagine you want to ensure code quality in a CI/CD pipeline for Python code that is pushed to a development branch. What kinds of checks would you implement in this pipeline to guarantee code quality? Please explain your approach and the tools or techniques you would use.

Are you interested in this opportunity and do you meet the criteria? Please get in touch with Marco Eindhoven of Global Enterprise Partners on telephone number or mail

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