Machine Learning Engineer
Indexed description
THE ROLE
We're looking for a hands-on and forward-thinking Machine Learning Engineer.
In this role, you will be supported by a dedicated team, equipped to aid your success and bolster our rapid growth. At Critical, we deliver software solutions and consulting in complex, business-critical environments aimed at assisting our clients in achieving their business objectives through cutting-edge software solutions.
The ideal candidate will design, build, and deploy machine learning models and pipelines that solve real business problems across various departments. You will work hands-on across the full ML lifecycle — from data preparation and model development to deployment and monitoring — collaborating closely with stakeholders to turn their needs into robust, production-ready solutions.
What You'll Do
- Design, train, and evaluate machine learning models to address specific business problems.
- Build and maintain data pipelines and infrastructure to support model development and deployment.
- Deploy ML models into production and monitor their performance, reliability, and drift over time.
- Identify and resolve technical issues, bugs, and blockers as they arise during development and deployment.
- Work closely with team members across various departments to understand their data, processes, and needs, and adapt solutions accordingly.
- Iterate on deployed models to keep them accurate, efficient, and useful as needs evolve.
- You adapt fast and turn sharp, critical thinking into good decisions for the project.
- You are genuinely curious and challenge what needs to change and make bold calls.
- You see AI as the biggest shift of this generation and you want to be part of shaping it.
- When there is no map, you start drawing one rather than waiting for someone else to.
- Bachelor's or Master's in Computer Science, AI, Machine Learning or a related field.
- 5+ years of experience in software development, with hands-on experience building and deploying machine learning models.
- Solid coding skills (Python and relevant ML frameworks) and experience with the ML lifecycle from data to production.
- Clear oral and written communication skills for working with teammates and stakeholders.
- Practical, detail-oriented approach to debugging and improving models and pipelines.
- Ability to manage your own tasks and prioritize effectively, even with some ambiguity.
- A natural interest in ML techniques — old and new — and how they apply to real business problems.
- Private health insurance
- Employee Assistance Programme (mental health, legal, financial support)
- Home office support
- Extra holidays: 2 additional days after year one, more as time goes on
- Extra parental leave: 2 additional months fully paid
- Gradual return-to-work support: We'll help you ease back from long breaks
- Sabbatical programme for long-term employees
- Training, mentorship, and growth opportunities: we'll invest in where you want to go next
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