Machine Learning Engineer

This company helps large organizations generate value from AI and data. They frequently observe that AI solutions get stuck in the prototype phase. Therefore, they assist organizations in building concrete AI solutions, moving them from prototype to production. They don't just build 'black box' models but offer complete solutions, from a modern data stack to robust MLOps, and from Generative AI to smart algorithms. This is achieved with a multidisciplinary team of experts: from data engineers and machine learning engineers to cloud specialists and DevOps engineers. They work with clients in domains such as e-commerce, banking, logistics, and manufacturing, utilizing various technologies including Python, AWS, Azure, GCP, Databricks, Spark, Kubernetes, Docker, and more.

Introduction

Haystack People is seeking a Machine Learning Engineer to build AI solutions that transition from prototypes to production. If you enjoy creating scalable AI platforms, production-grade ML systems, and Generative AI solutions that deliver measurable business impact, this role is for you.

Responsibilities

• Design, develop, and implement scalable and robust machine learning systems, AI products, and data platforms, from prototype to production.
• Collaborate with clients to understand their needs and deliver technically feasible solutions.
• Stay updated with the latest developments in machine learning, Generative AI, cloud technologies, and MLOps, and actively share knowledge with colleagues and the community.
• Mentor and coach junior colleagues, contributing to the continuous improvement of best practices.
• Contribute to innovative projects and the development of internal AI platforms and accelerators.

Requirements

• Completed a Master's degree (WO) in Computer Science, AI, Mathematics, or a related field.
• Minimum of 3 years of relevant work experience as a Machine Learning Engineer or similar role.
• In-depth knowledge of machine learning algorithms, frameworks (such as TensorFlow, PyTorch, Scikit-learn), and MLOps principles.
• Proficient in Python and experienced in the development and deployment of ML models.
• Experience with cloud platforms (AWS, Azure, or GCP) and containerization technologies (Docker, Kubernetes).
• A team player with excellent communication skills and a proactive attitude.
• Dutch language skills preferred (working knowledge required for this role based in the Netherlands).

Benefits

• An excellent salary between €5,000 and €7,000 per month, depending on knowledge and experience.
• A one-year contract with the prospect of a permanent contract.
• An extensive secondary benefits package, including a lease car/mobility budget, laptop, phone, and a pension scheme.
• A personal development budget for training, certifications, and conferences to stay at the forefront of your field.
• The opportunity to work on challenging and innovative projects for various top organizations in different sectors.
• A dynamic and inspiring work environment with a multidisciplinary team of driven experts, offering ample room for personal initiative and ideas.

About Company

This company helps large organizations generate value from AI and data. They frequently observe that AI solutions get stuck in the prototype phase. Therefore, they assist organizations in building concrete AI solutions, moving them from prototype to production. They don't just build 'black box' models but offer complete solutions, from a modern data stack to robust MLOps, and from Generative AI to smart algorithms. This is achieved with a multidisciplinary team of experts: from data engineers and machine learning engineers to cloud specialists and DevOps engineers. They work with clients in domains such as e-commerce, banking, logistics, and manufacturing, utilizing various technologies including Python, AWS, Azure, GCP, Databricks, Spark, Kubernetes, Docker, and more.

How to Apply

1. Apply for the position via our website, or directly through Tom.
2. Initial introductory interview with Tom, discussing your aspirations and where you would thrive best.
3. An interview with the company, delving deeper into the role's content and company culture.
4. A second interview, potentially including a case study.
5. Employment terms discussion.