Senior MLOps Engineer

At Cloudbeds, we're not just building software, we’re transforming hospitality. Our intelligently designed platform powers properties across 150 countries, processing billions in bookings annually.

Remote with expected travel into Paddington 2 days per week.

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’ll work closely with product and engineering teams to identify opportunities for improvement, develop innovative solutions, and drive revenue growth for hotels that rely on our platform.

Your impact will focus on ensuring the reliability, scalability, and high quality of our ML systems from development to production.

• Develop and implement end‑to‑end machine learning features that enable customers to optimize 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 like Prefect/Dagster) to define, schedule, and monitor complex data and ML workflows (DAGs).
• 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.

• 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 (e.g., data validation, model correctness, performance testing).
• 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 (e.g., clean code, version control, code reviews).
• 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.

• Experience with CI/CD tooling (e.g., Git Hub Actions, Jenkins) 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).

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.

• Best All‑In‑One Hotel Management System | Hotel Tech Awards  (2025)
• Overall 10 Best Places to Work | Hotel Tech Awards  (2025)
• Most Loved Workplace® Certified (2024)
• Top 10 People’s Choice (2024)
• Deloitte Technology Fast…