Consultant | Machine Learning Engineer | Public Sector

Who we are

ADC is a boutique Data and AI consultancy with offices in the Netherlands, Sweden, and Denmark, and an award-winning data design studio (CLEVER°FRANKE) embedded in the group. We help data-driven leaders in complex, high-stakes environments move fast without losing control. We deliver four core outcomes: growing revenue and margin, moving from AI pilots to scale, reducing risk while staying confidently compliant, and building data products people love to use.

We are a collective of statisticians, designers, PhDs, and agentic AI engineers with deep expertise in the industries we serve: financial services, life sciences, healthcare, the public sector, retail, agriculture, and transportation. We do not arrive with generic tools and a slide deck. We walk in, work alongside the people who know the organisation best, and build solutions that actually run.
We call ourselves Wayfinders. Our clients trust us to navigate complexity and make it real: turning ambition into action in environments where the margin for error is small and the consequences of getting it wrong are real, for customers, employees, supply chains, and the bottom line.

Shape the Future of AI-Powered Public services with ADC

We're leveraging our expertise in machine learning, cloud engineering, and MLOps to power ADC's growth and deliver unmatched value to our clients.
Our Public & Society team is growing, and we are looking for a Machine Learning Engineer, who is curious, technically driven, and eager to grow with us. In this role, you will design, build, deploy, and maintain production-grade machine learning solutions while applying modern software engineering practices to real-world public sector challenges. You will contribute to data and AI initiatives for public institutions, developing scalable systems that help improve public services, optimise operations, and enable data-driven decision-making.

You'll be part of a cross-functional team, working alongside data engineers, data scientists, machine learning engineers, and public sector stakeholders to bring machine learning solutions from experimentation to production. You'll help build scalable machine learning platforms, deploy robust ML solutions, and ensure they operate reliably, securely, and responsibly.

You will work in highly regulated environments where privacy, traceability, and reliability are critical requirements.

Where you'll make an impactBuild Machine Learning solutions for Public Sector challenges

• Design, develop, deploy, and maintain machine learning systems that address complex challenges within public sector organisations.

• Build scalable, secure, and reliable solutions that support critical public services and operational processes.

• Translate business challenges into practical, production-ready machine learning applications that create measurable impact.

Engineer robust Machine Learning systems for production

• Develop and maintain end-to-end machine learning pipelines covering data preparation, feature engineering, model training, deployment, monitoring, and continuous improvement.

• Apply MLOps best practices using technologies such as Azure, Databricks, MLflow, Docker, Airflow, and CI/CD tooling.

• Ensure machine learning systems are scalable, resilient, observable, and maintainable in production.

Deliver responsible and secure AI

• Build and implement machine learning solutions that meet high standards for security, privacy, transparency, and governance.

• Contribute to architectures that support regulated and mission-critical public sector environments.

• Implement model governance, monitoring, explainability, and compliance practices throughout the machine learning lifecycle.

Collaborate and drive continuous improvement

• Work closely with engineers, architects, data scientists, and client teams to deliver end-to-end machine learning solutions.

• Communicate complex technical concepts clearly to both technical and non-technical stakeholders.

• Continuously improve engineering practices, architectures, and ways of working to deliver sustainable, high-quality outcomes.

What you bring

At ADC, we're not just consultants. We're leading experts in Data & AI, using our expertise in strategy, business operations, and advanced analytics. What unites us is a shared way of working: breaking down complex challenges to change how organisations operate.

For this role, that means:

• A strong technical academic background: Computer Science, Machine Learning, Artificial Intelligence, Software Engineering, Data Engineering, or a similar technical field.

• Relevant professional experience: 3+ years of experience in Machine Learning Engineering, AI Engineering, Data Engineering with an ML focus, or a similar role, preferably within large and complex organisations.

• Hands-on experience across the machine learning lifecycle: Building, deploying, and maintaining production-grade machine learning solutions using Python and modern software engineering practices. Experience across the machine learning lifecycle, including data preparation, feature engineering, model development, evaluation, deployment, monitoring, and continuous improvement.

• Familiarity with cloud platforms (Azure, AWS), MLOps practices, CI/CD workflows, model governance, and tools for model tracking, experimentation, and deployment (e.g. MLflow, Databricks, Azure Machine Learning, or similar technologies).

• Experience working with data and regulated environments: Understanding of data pipelines, distributed processing, APIs, scalable data architectures, orchestration frameworks, and delivering solutions in public sector environments where privacy, traceability, governance, and reliability are essential.

• Experience implementing Generative AI solutions, including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and AI agents, is considered a plus.

• Strong communication and collaboration skills: Able to translate complex technical concepts into clear, actionable recommendations for both technical and non-technical audiences, from engineering teams to senior public sector stakeholders.

• A proactive and collaborative mindset: You enjoy solving challenging problems, driving innovation, and working closely with others to deliver meaningful outcomes.

• Fluency in English and Dutch is required.

• Willingness to travel within the Netherlands.

What's in it for you

At ADC, you're part of the collective from day one. We want you to grow, experiment, and shape your own path while working on projects that matter. That means:

• Impact you can point to: You'll see your work in action as AI solutions create measurable value for government organizations, public institutions, and the people they serve.

• Real responsibility, real support: You'll work alongside experienced experts from the start, learning by doing with mentorship and feedback built in.

• The freedom to try new things: Spot a better way to build something? A smarter process? We want people who challenge the status quo.

• Collective Week: Once a year, we go on a workation with the whole ADC team. We work on pro bono projects, explore new ideas, and make the kind of memories you'll want to talk about.

• Ownership opportunities: We offer a competitive salary and bonus structure, plus the opportunity to invest in the company and become a co-owner once specific conditions are met. We mean it when we say collective.

• A vibrant culture: Daily lunches, Friday drinks, sports, social events: a culture you'll notice from day one.

What to do next

If you believe in building with heart, thrive on collaboration, and want to grow with a team that challenges and supports you, we want to meet you! If this sounds like your next challenge, apply through the link below.

*Amsterdam Data Collective is an equal opportunity employer. To shape a better future, we value diversity and promote equality. No characteristics are discriminated against, as indicated in Directive 2000/78/EC. We encourage and welcome all applications. Additional arrangements or adjustments to support your application can be discussed further.