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For Companies At a Glance
- Tasks: Design, build, and optimise machine learning models for innovative data-driven solutions.
- Company: Join a forward-thinking tech company focused on AI and machine learning.
- Benefits: Enjoy remote work flexibility, competitive salary, and opportunities for professional growth.
- Why this job: Make an impact in the exciting field of machine learning and AI.
- Qualifications: 3+ years in ML, strong Python skills, and experience with ML frameworks.
- Other info: Collaborative environment with a focus on continuous learning and innovation.
The predicted salary is between 36000 - 60000 £ per year.
We are seeking a highly skilled Machine Learning Engineer to design, build, deploy, and optimise machine learning models that power data-driven products and business solutions. This role bridges data science and software engineering, focusing on production-ready ML systems, scalability, and performance. The ideal candidate has strong experience in Python, ML frameworks, data pipelines, and cloud platforms, and is comfortable working in a fully remote, collaborative environment within the UK.
Key Responsibilities
- Machine Learning Model Development
- Design, develop, train, and evaluate machine learning models for prediction, classification, recommendation, or automation use cases.
- Apply supervised, unsupervised, and deep learning techniques as appropriate.
- Perform feature engineering, model tuning, and validation to improve accuracy and performance.
- Productionisation & Deployment
- Deploy ML models into production using scalable, reliable architectures.
- Build and maintain APIs or batch pipelines for model inference.
- Monitor model performance, data drift, and retraining needs.
- Data Engineering & Pipelines
- Collaborate with data engineers to design efficient data ingestion and transformation pipelines.
- Work with structured and unstructured data from databases, APIs, and data lakes.
- Ensure data quality, reproducibility, and versioning.
- MLOps & Automation
- Implement MLOps practices including CI/CD for ML, model versioning, and experiment tracking.
- Use tools such as MLflow, Kubeflow, SageMaker, Vertex AI, or Azure ML.
- Automate model training, testing, deployment, and monitoring workflows.
- Cloud & Infrastructure
- Build ML solutions on cloud platforms such as AWS, Azure, or GCP.
- Use containerization and orchestration tools (Docker, Kubernetes).
- Optimize compute costs and performance for training and inference workloads.
- Collaboration & Stakeholder Engagement
- Work closely with Data Scientists, Product Managers, Software Engineers, and Analysts.
- Translate business requirements into scalable ML solutions.
- Communicate model behaviour, limitations, and results clearly to non-technical stakeholders.
- Research & Continuous Improvement
- Stay current with advancements in machine learning, AI, and data science.
- Evaluate new algorithms, tools, and frameworks for potential adoption.
- Contribute to best practices, documentation, and knowledge sharing.
Required Skills & Experience
- Core Technical Skills
- 3+ years of experience in Machine Learning, Data Science, or related roles.
- Strong programming skills in Python.
- Experience with ML frameworks: TensorFlow, PyTorch, scikit-learn, XGBoost.
- Solid understanding of ML algorithms, statistics, and evaluation metrics.
- Experience deploying ML models into production environments.
- Data & Engineering Skills
- Strong SQL skills and experience working with large datasets.
- Familiarity with data processing tools (Pandas, NumPy, Spark).
- Experience building APIs (FastAPI, Flask) for ML services.
Machine Learning Engineer in Cardiff employer: iConsultera
Contact Detail:
iConsultera Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer in Cardiff
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and join online communities. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects. Whether it's a GitHub repo or a personal website, having tangible examples of your work can really set you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on common ML concepts and algorithms. Practice explaining your past projects and how you tackled challenges. Confidence and clarity can make a huge difference!
✨Tip Number 4
Don't forget to apply through our website! We love seeing candidates who are genuinely interested in joining our team. Plus, it’s the best way to ensure your application gets the attention it deserves.
We think you need these skills to ace Machine Learning Engineer in Cardiff
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with Python, ML frameworks, and data pipelines. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects or achievements!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about machine learning and how you can contribute to our team. Keep it conversational but professional – we love a bit of personality!
Showcase Your Projects: If you've worked on any cool machine learning projects, make sure to mention them in your application. Whether it's a personal project or something from work, we want to see your hands-on experience and creativity!
Apply Through Our Website: We encourage you to apply directly through our website for the best chance of getting noticed. It’s super easy, and you’ll be one step closer to joining our awesome team at StudySmarter!
How to prepare for a job interview at iConsultera
✨Know Your ML Models Inside Out
Make sure you can discuss the machine learning models you've worked on in detail. Be prepared to explain your approach to model development, including feature engineering and tuning techniques. This shows your depth of knowledge and hands-on experience.
✨Showcase Your Deployment Skills
Since deployment is key for this role, be ready to talk about how you've deployed ML models in the past. Discuss the architectures you've used, any APIs you've built, and how you monitor model performance post-deployment. This will demonstrate your practical skills in productionising ML solutions.
✨Familiarise Yourself with MLOps Tools
Brush up on MLOps practices and tools like MLflow, Kubeflow, or SageMaker. Be prepared to discuss how you've implemented CI/CD for ML projects and how you track experiments. This will show that you're not just a coder but also understand the operational side of machine learning.
✨Communicate Clearly with Non-Technical Stakeholders
Practice explaining complex concepts in simple terms. You might need to translate technical details for product managers or analysts, so being able to communicate effectively is crucial. Think of examples where you've successfully done this in the past.