At a Glance
- Tasks: Build and implement AI-powered features to optimise hotel revenue strategies.
- Company: Join Cloudbeds, a leader in transforming hospitality with innovative tech solutions.
- Benefits: Enjoy remote work, competitive pay, wellness Fridays, and professional development opportunities.
- Why this job: Make a real impact in the hospitality industry using cutting-edge machine learning techniques.
- Qualifications: 3+ years in data engineering or machine learning, strong Python and SQL skills required.
- Other info: Be part of a diverse, global team passionate about innovation and travel.
The predicted salary is between 36000 - 60000 £ per year.
At Cloudbeds, we are transforming hospitality. Our platform powers properties across 150 countries, processing billions in bookings annually. We help hoteliers transform operations and uplevel their commercial strategy through a unified platform that integrates with hundreds of partners. Imagine working alongside global innovators to build AI-powered solutions that solve hoteliers' biggest challenges.
Location: Remote with expected travel into Paddington 2 days per week.
How You’ll Make an Impact:
As a Machine Learning Ops Engineer, you will play a key role in building and implementing features that empower lodging customers to make data-driven pricing decisions. You will work closely with product and engineering teams to identify opportunities for improvement, develop innovative solutions, and drive revenue growth for the hotels that rely on our platform. Your impact will be focused on ensuring the reliability, scalability, and high quality of our ML systems from development to production. You will be instrumental in establishing robust MLOps practices and rigorous testing processes across the entire ML lifecycle.
What You Bring to the Team:
- Develop and implement end-to-end machine learning features that enable customers to optimize their revenue strategies, with a strong emphasis on production readiness and system reliability.
- Establish and maintain robust MLOps practices including CI/CD for model training, testing, deployment, and monitoring.
- Design, build, and maintain highly reliable and well-tested data and ML pipelines to extract, transform, and structure large datasets for ML applications.
- Expertise in using Apache Airflow (or similar orchestration tools) to define, schedule, and monitor complex data and ML workflows.
- Implement comprehensive software quality and testing processes for ML systems, including unit, integration, and end-to-end testing for both code and data/model performance.
- Design, train, and rigorously test machine learning models where needed to improve pricing optimization, focusing on statistical validation and production stability.
- Implement model performance monitoring (e.g., drift detection, data quality checks) to ensure deployed models maintain accuracy and relevance over time.
- Collaborate cross-functionally with product, engineering, and data science teams to define SLIs/SLOs for ML services and improve system performance, stability, and usability.
- Conduct structured A/B testing and experimentation to validate model effectiveness and continuously improve performance, documenting results and sharing technical insights.
What Sets You Up for Success:
- Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field.
- 3+ years of experience in a data engineering or machine learning role, with demonstrated success in MLOps and deploying models to production.
- Proven expertise in designing and implementing ML testing strategies.
- Expertise in deploying ML models at scale on AWS, with experience using MLFlow or similar platforms.
- Strong Python programming skills and adherence to software engineering best practices.
- Expert-level SQL skills and experience working with large datasets for analysis and modeling.
- Strong problem-solving skills with the ability to apply creative, data-driven solutions to complex business challenges.
- Excellent communication and collaboration skills, with experience working cross-functionally with product and engineering teams.
Bonus Skills to Stand Out (Optional):
- Experience with CI/CD tooling specifically for ML pipelines and Airflow DAG deployment.
- Experience with data quality monitoring tools and frameworks.
- Master's or PhD in Computer Science, Data Science, or a related field.
- Relevant certifications (AWS, MLFlow, or other data science/ML certifications).
What to Expect - Your Journey with Us:
Behind Cloudbeds' revolutionary technology is a team redefining what’s possible in hospitality. We’re 650+ employees across 40+ countries, bringing together elite engineers, AI architects, world-class designers, and hospitality veterans to solve challenges others haven’t dared to tackle. Our diverse team speaks 30+ languages, but we all share one language: a passion for innovation and travel.
Cloudbeds is proud to be an Equal Opportunity Employer that celebrates the diversity in our global team! We do not discriminate based upon race, religion, color, national origin, gender, sexual orientation, gender identity, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics.
MLOps Engineer in Westminster employer: Cloudbeds
Contact Detail:
Cloudbeds Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land MLOps Engineer in Westminster
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those already at Cloudbeds. A friendly chat can open doors and give you insider info on what they're really looking for.
✨Tip Number 2
Show off your skills! If you've got a portfolio or GitHub with projects related to MLOps, make sure to highlight them during interviews. Real-world examples speak volumes about your capabilities.
✨Tip Number 3
Prepare for technical challenges! Brush up on your Python and ML concepts, and be ready to tackle some coding problems. We love seeing how you think through solutions, so practice makes perfect!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you're genuinely interested in joining our awesome team.
We think you need these skills to ace MLOps Engineer in Westminster
Some tips for your application 🫡
Tailor Your Application: Make sure to customise your CV and cover letter for the MLOps Engineer role. Highlight your experience with machine learning, data pipelines, and any relevant tools like Apache Airflow. We want to see how your skills align with what we do at Cloudbeds!
Showcase Your Projects: Include specific examples of projects you've worked on that demonstrate your expertise in MLOps and machine learning. Whether it's a personal project or something from a previous job, we love seeing real-world applications of your skills!
Be Clear and Concise: When writing your application, keep it clear and to the point. Use bullet points where possible to make it easy for us to read. We appreciate straightforward communication, so don’t be afraid to show your personality while being professional!
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’re considered for the role. Plus, it gives you a chance to explore more about our culture and values!
How to prepare for a job interview at Cloudbeds
✨Know Your MLOps Inside Out
Make sure you brush up on your MLOps knowledge before the interview. Understand the key concepts like CI/CD for model training, testing, and deployment. Be ready to discuss how you've implemented these practices in past projects.
✨Showcase Your Problem-Solving Skills
Prepare to share specific examples of how you've tackled complex challenges in machine learning. Think about times when you had to apply creative, data-driven solutions to improve system performance or reliability.
✨Familiarise Yourself with Their Tech Stack
Cloudbeds uses tools like Apache Airflow for orchestration, so it’s a good idea to get comfortable with these technologies. If you have experience with similar tools, be sure to highlight that during your chat.
✨Communicate Effectively
Since collaboration is key at Cloudbeds, practice articulating your thoughts clearly. Be prepared to explain technical concepts in a way that non-technical team members can understand, showcasing your excellent communication skills.