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For Companies At a Glance
- Tasks: Design and deploy cutting-edge machine learning models for innovative data-driven solutions.
- Company: Join a forward-thinking tech company with a focus on collaboration and innovation.
- Benefits: Enjoy competitive pay, remote work flexibility, and opportunities for professional growth.
- Why this job: Make a real impact in the AI field while working with the latest technologies.
- Qualifications: 3+ years in machine learning, strong Python skills, and experience with ML frameworks.
- Other info: Dynamic remote environment with excellent career advancement opportunities.
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 Norwich employer: iConsultera
Contact Detail:
iConsultera Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer in Norwich
✨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 us at StudySmarter. Tailor your application to highlight how your skills align with our needs.
We think you need these skills to ace Machine Learning Engineer in Norwich
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 ML projects, make sure to mention them! Whether it's a personal project or something from a previous job, we want to see your hands-on experience and creativity in action.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates. Plus, it’s super easy!
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
Be ready to talk about how you've deployed ML models in production. Discuss the architectures you've used, any APIs you've built, and how you've monitored model performance. This will demonstrate your ability to bridge the gap between data science and software engineering.
✨Familiarise Yourself with MLOps Practices
Understand the MLOps tools and practices relevant to the role, such as CI/CD for ML and model versioning. If you've used tools like MLflow or Kubeflow, be sure to mention them. This will highlight your readiness to implement efficient workflows.
✨Communicate Clearly with Non-Technical Stakeholders
Prepare to explain complex concepts in simple terms. Think of examples where you've had to translate technical details for non-technical team members. This skill is crucial for collaboration and stakeholder engagement, so make it a point to showcase it.