AI ML Senior Engineer

GeekSoft Consulting; Amsterdam; On-site; EUR 70,000 - 100,000; Full time

Benefits offered by this job

Challenging, innovative environment

Learning opportunities

Job summary

A technology consulting firm in the Netherlands is seeking an experienced AI/ML Senior Engineer to join their growing ML Engineering team. This role involves designing and deploying production-grade machine learning systems while ensuring reliability and performance. The successful candidate will work closely with data scientists and use modern cloud-native technologies like Azure and AWS. Strong proficiency in Python, knowledge of CI/CD practices, and experience with ML tools are essential for this challenging position. Opportunities for continuous learning are provided.

Qualifications

• Proven experience in ML Engineering, MLOps, or Data Engineering.
• Hands-on experience in building and maintaining ML workflows and pipelines.
• Strong proficiency in Python, with experience in ML frameworks.

Responsibilities

• Design, deploy, and operate production-grade machine learning systems.
• Maintain ML pipelines for model training and deployment.
• Collaborate with cross-functional teams to productionize models.

Skills

MLOps

ML platform development

Python

Azure

AWS

CI/CD

Machine learning

Collaboration

Tools

Docker

Terraform

Airflow

GitHub Actions

MLflow

Job description

• Help design, build and continuously improve the clients online platform.
• Research, suggest and implement new technology solutions following best practices/standards.
• Take responsibility for the resiliency and availability of different products.
• Be a productive member of the team.

Requirements

• AI/ML Senior Engineer to join our growing ML Engineering team.
• Collaborate closely with data scientists, engineers, and product managers to design, deploy, and operate production‑grade machine learning systems that power critical services across our Digital and Retail platforms.
• This position has a strong focus on MLOps and ML platform development, helping scale and maintain reliable, end‑to‑end ML workflows using modern cloud‑native infrastructure and tools.
• Design, build, and maintain ML pipelines for model training, validation, deployment, and monitoring.
• Enable scalable ML solutions for use cases such as recommendation systems, forecasting, and intelligent automation.
• Develop and deploy production‑ready services using tools such as Airflow, Azure ML, and FastAPI.
• Automate model build and deployment workflows using CI/CD pipelines (GitHub Actions, Azure DevOps).
• Ensure reliability, observability, and performance of the ML platform.
• Collaborate with data scientists to productionize research models and code into scalable services.
• Implement monitoring, alerting, and model drift detection using tools like Azure Monitor, New Relic, Grafana, and custom logging frameworks.
• Continuously improve and manage cloud infrastructure using Terraform, Docker, and Fargate.
• Proven experience in ML Engineering, MLOps, DevOps, or Data Engineering with exposure to the full ML lifecycle.
• Hands‑on experience building and maintaining ML workflows and pipelines.
• Strong proficiency in Python, with experience using MLflow, Scikit‑learn, or PyTorch.
• Experience with cloud platforms, particularly Azure and/or AWS.
• Solid understanding of containerization (Docker) and orchestration technologies such as Kubernetes.
• Hands‑on exposure to CI/CD tools (GitHub Actions, Azure DevOps) and Infrastructure as Code (Terraform).
• Strong collaboration and communication skills, with the ability to work in cross‑functional teams.
• Languages: Python (primary), SQL, Bash
• Cloud Platforms: Azure, AWS
• ML & Workflow Tools: MLflow, Azure ML, Airflow
• APIs & Services: FastAPI, Azure Functions
• Data Platforms: Snowflake, Delta Lake, Redis, Azure Data Lake
• Infrastructure & DevOps: Docker, Fargate, Terraform, GitHub Actions, Azure DevOps
• Monitoring & Observability: Grafana, Azure Monitor, New Relic
• Experience working with enterprise data platforms such as Snowflake or Azure Data Lake.
• Experience deploying ML models as APIs or microservices.
• Strong understanding of model performance tracking, monitoring, and observability best practices.
• Familiarity with orchestration tools such as Airflow or Azure Data Factory.

Benefits

• A challenging, innovating environment.
• Opportunities for learning where needed.