At a Glance
- Tasks: Design and optimise machine learning models for user personalisation and deploy them in production.
- Company: Join a dynamic team focused on innovation and growth in AI technology.
- Benefits: Enjoy competitive pay, flexible work options, and opportunities for professional development.
- Other info: Collaborate with enthusiastic teams and explore cutting-edge research in a fast-paced environment.
- Why this job: Be at the forefront of AI, making a real impact on user experiences and business success.
- Qualifications: Expertise in machine learning, Python, and experience with data processing and model serving.
The predicted salary is between 70000 - 90000 £ per year.
Requirements
- 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
- (Desirable) Experience working on OTT platforms
- (Desirable) Experience in Scala
What the job involves
- 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
Senior AI Engineer (12 Month Fixed Term Contract) in London employer: Fractal
As a Senior AI Engineer at our innovative company, you'll thrive in a dynamic environment that champions creativity and collaboration. We offer competitive benefits, a supportive work culture that values continuous learning, and ample opportunities for professional growth, all while being part of a team that is passionate about pushing the boundaries of technology in a vibrant location. Join us to make a meaningful impact in the world of machine learning and personalisation.
StudySmarter Expert Advice🤫
We think this is how you could land Senior AI Engineer (12 Month Fixed Term Contract) in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with other AI enthusiasts. 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, especially those involving recommendation systems or real-time data processing. This will give potential employers a taste of what you can do.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Be ready to discuss your experience with ML libraries and frameworks, and don’t forget to highlight your communication skills!
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who are genuinely interested in joining our team of enthusiastic over-achievers.
We think you need these skills to ace Senior AI Engineer (12 Month Fixed Term Contract) in London
Some tips for your application 🫡
Showcase Your Expertise:Make sure to highlight your experience with the full lifecycle of machine learning in your application. We want to see how you've developed, deployed, and maintained models, so don’t hold back on those details!
Be Specific About Your Skills:When mentioning your proficiency in Python and ML libraries, be specific about which ones you’ve used. If you’ve worked with TensorFlow or PyTorch, give examples of projects where you applied these skills. We love seeing real-world applications!
Highlight Collaboration Experience:Since we value cross-functional collaboration, share any experiences where you’ve worked with different teams. This could be anything from aligning machine learning initiatives with business goals to engaging with product teams. It shows us you’re a team player!
Apply Through Our Website:We encourage you to apply through our website for a smoother process. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates. Plus, we can’t wait to hear from you!
How to prepare for a job interview at Fractal
✨Know Your ML Lifecycle
Make sure you can confidently discuss the full lifecycle of machine learning. Be prepared to share specific examples from your experience in model development, deployment, and monitoring. This will show that you understand the nuances of the role and can hit the ground running.
✨Showcase Your Technical Skills
Brush up on your Python skills and be ready to talk about your experience with ML libraries like TensorFlow and PyTorch. If you've worked with ML training frameworks or model serving technologies, have some concrete examples ready to demonstrate your proficiency.
✨Highlight Your Data Pipeline Experience
Since the role involves constructing and maintaining data pipelines, be prepared to discuss your experience with high-volume data processing and real-time streaming architectures. Share any challenges you've faced and how you overcame them to showcase your problem-solving skills.
✨Communicate Effectively
Good communication is key, especially when collaborating with cross-functional teams. Practice explaining complex concepts in simple terms, and be ready to discuss how you've worked with others to align machine learning initiatives with business objectives. This will demonstrate your ability to work well in a team environment.
