Machine Learning​/AI Engineer

This is your opportunity to work on meaningful problems that directly impact patient care!

You will work in a collaborative team environment with opportunities for professional growth. You will work at the forefront of responsible AI in healthcare with continuous learning opportunities in both ML/AI technologies and healthcare domain knowledge.

We value versatility and growth potential. If your profile aligns with the majority of the requirements below and you are passionate about applying AI to healthcare, we encourage you to apply even if you do not check every box.

We're particularly interested in candidates who demonstrate:

• Strong fundamentals in ML and software engineering.
• Ability to both research/experiment and ship production code.
• Genuine interest in healthcare applications and responsible AI.

Education and Experience:

• BSc, MSc, or PhD in Computer Science, Electrical Engineering, Biomedical Engineering, Mathematics, Physics, or related fields
• 2+ years of experience shipping ML code to production
• Demonstrated experience with the full ML lifecycle: from experimentation to production deployment

Technical

Skills:

• Strong programming skills in Python, with deep experience in ML frameworks such as PyTorch, Tensor Flow, JAX, and Scikit-learn.
• Solid understanding of machine learning algorithms, statistics, and mathematical foundations.
• Experience with model training, optimization, and hyperparameter tuning.

LLMs and GenAI:

• Hands-on experience with LLM fine tuning and adaptation techniques such as LoRA, QLoRA, PEFT, instruction tuning, and prompt engineering.
• Experience with GenAI architectures and workflows such as RAG, agentic systems, and retrieval mechanisms.
• Familiarity with LLM tooling and frameworks including Hugging Face Transformers, Langchain/Llama Index, and vector databases.
• Not just API integration; we need someone who understands how these models work under the hood.

Software Engineering & MLOps:

• Strong software development practices including test-driven development, code review, version control with Git, and documentation.
• Writing clean, maintainable, production-quality code.
• Experience with MLOps tools and practices such as MLflow, Kubeflow, DVC, and Weights & Biases.
• CI/CD pipeline development and management for ML systems.
• Containerization and orchestration using tools such as Docker and Kubernetes.
• Experience with cloud platforms such as AWS, GCP, or Azure and hybrid cloud/on-prem infrastructure.
• Strong focus on model evaluation, validation, and monitoring in production environments.

We are looking for a versatile ML/AI Engineer who can work across the full AI stack. You should be comfortable with both fine-tuning foundation models and shipping production-ready solutions with strong MLOps practices.

Main responsibilities:

Model Development and Deployment

• Design, train, finetune, adapt and evaluate AI models, including foundation models, to improve healthcare processes.
• Implement model fine tuning and customization techniques, going beyond API integration to deep model adaptation.
• Productionize models and integrate them into clinical systems and databases.
• Implement scalable ML solutions using MLOps principles, including CI/CD, monitoring, and versioning.

Model Validation and Evaluation

• Develop robust evaluation frameworks tailored to healthcare applications.
• Work with clinical stakeholders to validate model outputs and iterate based on feedback.
• Monitor and optimize model performance in production and implement comprehensive testing strategies to ensure reliability, safety, and scalability.

Collaboration and Communication

• Work closely with cross-functional teams including data scientists, ML engineers, clinicians, and product managers.
• Translate complex technical concepts for non-technical clinical stakeholders.

Other Responsibilities:

Innovation and Experimentation

• Experiment with state-of-the-art algorithms, particularly in LLM applications and clinical NLP.
• Stay current with advances in machine learning, LLMs, MLOps, and responsible AI practices.
• Contribute to continuous improvement of MLOps practices and infrastructure.

Data Handling and Analysis

• Preprocess large clinical datasets such as structured EHR data, clinical notes, and medical protocols.
• Conduct exploratory data analysis and feature engineering for healthcare applications.

You will join a collaborative team of 5 members, of which 3 are data scientists, a Product Owner and one Tech Lead.

Our AI team builds AI solutions that directly impact patient care and clinical decision-making. Working at the intersection of cutting-edge machine learning and clinical medicine, we develop models for clinical NLP, decision support systems, and predictive analytics that help healthcare professionals deliver better care. We build custom solutions and leverage out-of-the-box tools such as LLM models, agents, and RAG systems.

At Haga Ziekenhuis, we understand that patients come to us at a vulnerable moment in their lives. They place their trust in our care. We take that trust seriously by…