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- Tasks: Design, build, and optimise machine learning models for innovative data-driven solutions.
- Company: Join a forward-thinking tech company with a focus on collaboration and innovation.
- Benefits: Enjoy remote work flexibility, competitive salary, and opportunities for professional growth.
- Why this job: Make an impact in the AI field while working with cutting-edge 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.
- 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 Leicester employer: iConsultera
Contact Detail:
iConsultera Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer in Leicester
✨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 those interviews! Brush up on common ML concepts and be ready to discuss your past projects in detail. Practising coding challenges can also help you feel more confident when it’s time to shine.
✨Tip Number 4
Don’t forget to apply through our website! We love seeing applications come directly from passionate candidates. Plus, it gives you a better chance to stand out in the hiring process.
We think you need these skills to ace Machine Learning Engineer in Leicester
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Machine Learning Engineer role. Highlight your experience with Python, ML frameworks, and any relevant projects you've worked on. We want to see how your skills align with what we're looking for!
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. Be sure to mention any specific experiences that relate to the job description.
Showcase Your Projects: If you've worked on any machine learning projects, make sure to include them in your application. Whether it's a personal project or something from a previous job, we love seeing practical examples of your work and problem-solving skills.
Apply Through Our Website: We encourage you to apply through our website for a smoother process. It helps us keep track of applications and ensures you get all the updates directly from us. Plus, it shows you're keen on joining StudySmarter!
How to prepare for a job interview at iConsultera
✨Know Your 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 that you not only understand the theory but also have practical experience.
✨Showcase Your Deployment Skills
Since this role involves deploying ML models, be ready to talk about your experience with productionisation. Discuss any tools you've used like Docker or Kubernetes, and how you've built APIs for model inference. Highlighting your MLOps knowledge will definitely impress.
✨Collaborate Like a Pro
This position requires working closely with various teams. Prepare examples of how you've collaborated with data engineers, product managers, or software engineers in the past. Emphasise your communication skills, especially when translating technical concepts to non-technical stakeholders.
✨Stay Updated and Curious
Demonstrate your passion for continuous learning in the field of machine learning. Share any recent advancements or tools you've explored, and be ready to discuss how they could benefit the company. This shows you're proactive and committed to staying at the forefront of technology.