At a Glance
- Tasks: Design and optimise machine learning models for user personalisation and data pipeline engineering.
- Company: Leading AI company in West London with a hybrid work culture.
- Benefits: Competitive salary, flexible working, and opportunities for professional growth.
- Other info: Collaborative environment with exciting projects and career advancement opportunities.
- Why this job: Join a team at the forefront of AI innovation and make a real impact.
- Qualifications: Expertise in machine learning lifecycle and proficiency in Python required.
The predicted salary is between 70000 - 90000 £ per year.
What you’ll be doing
- Model Development: Design, train, and optimise machine learning models focused on user personalisation, encompassing recommendation engines, ranking algorithms, user segmentation, and content analysis.
- Data Pipeline Engineering: Construct and maintain robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets.
- Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance.
- Experimentation: Lead the design and analysis of A/B tests and offline experiments to evaluate model efficacy and support continuous improvement.
- Cross‑Functional Collaboration: Engage with multidisciplinary teams to align machine learning initiatives with business objectives and user needs.
- Research & Innovation: Evaluate emerging research in machine learning, deep learning, and personalisation for potential integration within existing systems.
What you’ll bring
- Demonstrated expertise in the full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance.
- Proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch).
- Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (e.g., TensorFlow Serving, Triton, TorchServe).
- Experience with high-volume data processing and real‑time streaming architectures.
- Strong understanding of recommendation system design and personalisation algorithms.
- Familiarity with Generative AI and its applications in production settings.
- Good communication and analytical problem‑solving skills.
Good to have
- Experience working on OTT platforms
- Experience in Scala
Senior AI Engineer employer: Dormont Manufacturing Co
Join a forward-thinking company in West London that champions innovation and collaboration, making it an exceptional employer for Senior AI Engineers. With a hybrid work model, employees enjoy the flexibility of working onsite just two days a week, fostering a balanced work-life environment. The company prioritises professional growth through continuous learning opportunities and encourages a culture of experimentation and cross-functional teamwork, ensuring that your contributions directly impact user experience and business success.
StudySmarter Expert Advice🤫
We think this is how you could land Senior AI Engineer
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Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Dormont Manufacturing Co.
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We think you need these skills to ace Senior AI Engineer
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Dormont Manufacturing Co, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Dormont Manufacturing Co. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Dormont Manufacturing Co
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
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Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Dormont Manufacturing Co!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
