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Senior ML Ops Engineer

Focused. Encouraging. Honest. We need your expertise to help us do something great for our travellers: make booking stays and journeys more sustainable and straightforward. This involves technical challenges and the latest technology, from machine learning and cloud services to world-class APIs!

At Skyscanner, we’re revolutionizing the travel experience with cutting-edge AI and data-driven solutions. As a Senior Engineer in the Machine Learning Operations squad, you’ll play a critical role in developing the infrastructure that empowers our data science teams to improve and deploy the models that are making an ever-greater contribution to our product experience. You’ll typically be working in Java or Python, and with a technology stack that includes AWS, Kinesis, S3, Kubernetes, Spark, Airflow, gRPC, New Relic, Databricks, and more.

This role requires expertise in distributed systems, microservices, and data pipelines, combined with a strong focus on observability and the ability to leverage vendor technologies to deliver impactful solutions. While this is not an ML development role, familiarity with the machine learning lifecycle is an advantage. Your ability to solve problems collaboratively with your teammates, and your passion to learn. You'll be able to break down problems into bite-size chunks and deliver them with high quality.

Key Responsibilities

  • Distributed Systems Development: Design and build scalable distributed systems using Java-based microservices and Python batch processing to support our ML models, evaluation, and observability.

  • Model Lifecycle: Create and maintain robust model deployment pipelines using PySpark and Databricks, ensuring efficient and reliable data flow across AI systems.

  • Vendor Integration: Identify and leverage vendor capabilities (e.g., AWS, Databricks, and other cloud services) to deliver high-quality solutions that align with organizational goals.

  • Observability Solutions: Develop monitoring and observability systems to track model performance, detect anomalies, and ensure outputs align with business and ethical standards.

  • Collaboration with Specialists: Work closely with cross-functional teams, including Security, Data Science and Product, to ensure comprehensive and secure solutions.

  • Knowledge Sharing: Act as a mentor and technical leader within the squad, fostering collaboration and growth among team members.

Complementary skills for this role

  • Technical Expertise: Extensive experience with distributed systems engineering, including designing and implementing Java-based microservices and Python batch jobs.

  • Observability Knowledge: Deep understanding of observability principles, including monitoring, logging, and real-time system insights
  • Data Engineering Skills: Proficiency in building data pipelines using PySpark and Databricks, with a strong understanding of data flow and processing.

  • Cloud Vendor Experience: Hands-on experience leveraging vendor technologies like AWS and Databricks to deliver scalable, robust solutions..

  • AI/ML Lifecycle Awareness: Familiarity with the machine learning lifecycle (e.g., tools like MLflow, Data Bricks Model Serving) and its integration into production systems.

  • Collaboration: Strong interpersonal skills with the ability to work effectively across teams, including specialists in security and data science.

  • Problem-Solving Skills: A proactive and innovative approach to tackling complex technical challenges.

Skyscanner is a hybrid working company and most roles can be either Full Time or Part Time. We believe when people meet regularly in person, we are better able to innovate, learn, collaborate and inspire. We ask people to be in the office on average 8 days per month.

Already a global leader in travel, we want to elevate the way we work to a whole other level. In return, you’ll get meaningful things like medical insurance, headspace subscriptions, a home office allowance, and the option to buy more holiday. You’ll have the opportunity to work from any country for 4 weeks a year, and 30 days in our other global offices. Everything, in other words, to help you relax and give your best.For more details on Engineering at Skyscanner, check our Engineering Blog and follow Skyscanner Engineering on Twitter.

Average salary estimate

$100000 / YEARLY (est.)
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$80000K
$120000K

If an employer mentions a salary or salary range on their job, we display it as an "Employer Estimate". If a job has no salary data, Rise displays an estimate if available.

What You Should Know About Senior ML Ops Engineer, Skyscanner

At Skyscanner, we’re on a mission to revolutionize the travel experience, and we want you to be a part of it as our Senior ML Ops Engineer in Edinburgh! Our focus is clear: help travelers book stays and journeys more sustainably and straightforwardly. This role presents an exciting opportunity where your expertise in machine learning operations and cloud services will come into play. You’ll be diving into technical challenges, utilizing top-notch technologies like Java, Python, AWS, Kinesis, and Kubernetes to develop the infrastructure that empowers our brilliant data science teams. Your primary responsibility will be to design and build scalable distributed systems that ensure our models run smoothly. Collaborating closely with cross-functional teams, including Security and Data Science, you'll develop monitoring systems to track performance metrics and align our outputs with business ethics. Plus, you’ll find joy in mentoring and sharing your technical knowledge with your teammates, fostering an environment of growth and collaboration. We value problem-solving capabilities and a passion for continuous learning. At Skyscanner, we offer flexible working arrangements because we believe that regular in-person interactions enhance our creativity and collaboration. You can expect a supportive environment designed to help you thrive, including great benefits like medical insurance, a home office allowance, and the chance to work from various locations throughout the year. So, are you ready to elevate your career with us? Let’s make travel better together!

Frequently Asked Questions (FAQs) for Senior ML Ops Engineer Role at Skyscanner
What are the key responsibilities of a Senior ML Ops Engineer at Skyscanner?

As a Senior ML Ops Engineer at Skyscanner, your responsibilities include designing and building scalable distributed systems using Java and Python, creating and maintaining deployment pipelines with PySpark and Databricks, integrating vendor capabilities like AWS to deliver high-quality solutions, and developing observability systems to track model performance.

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What qualifications are required to become a Senior ML Ops Engineer at Skyscanner?

To become a Senior ML Ops Engineer at Skyscanner, candidates should possess extensive experience with distributed systems, proficiency in Java and Python, knowledge of observability principles, and hands-on experience with cloud technologies like AWS and Databricks. Familiarity with the machine learning lifecycle is also advantageous.

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How does collaboration work for a Senior ML Ops Engineer at Skyscanner?

At Skyscanner, collaboration for a Senior ML Ops Engineer is key. You’ll work closely with cross-functional teams, including Security, Data Science, and Product, to deliver comprehensive and secure solutions, sharing insights and fostering teamwork across departments.

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What technology stack does Skyscanner use for the Senior ML Ops Engineer position?

The technology stack for the Senior ML Ops Engineer role at Skyscanner includes AWS, Kinesis, S3, Kubernetes, PySpark, Databricks, Spark, Airflow, and more, enabling you to work with leading technologies to enhance our machine learning capabilities.

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What opportunities for professional growth exist for a Senior ML Ops Engineer at Skyscanner?

Skyscanner provides ample opportunities for professional growth for Senior ML Ops Engineers, including mentorship roles, the chance to lead technical initiatives, and access to training in the latest technologies and industry practices, supporting your career advancement.

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Common Interview Questions for Senior ML Ops Engineer
Can you explain your experience with distributed systems engineering related to the Senior ML Ops Engineer role?

When discussing your experience, emphasize specific projects where you designed and implemented distributed systems. Highlight the technologies used, challenges faced, and how your solutions improved system performance or scalability.

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How do you ensure observability in systems you're developing as a Senior ML Ops Engineer?

Address this by discussing the monitoring, logging, and alerting strategies you've employed in past projects. Elaborate on tools used for observability and how you ensure that both performance and anomalies are effectively tracked.

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What is your approach to building data pipelines using PySpark and Databricks?

Outline your systematic approach to building data pipelines, including your understanding of ETL processes. Talk about how you manage data flow, integration challenges, and ensure reliability in data processing.

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Discuss a time when you collaborated with cross-functional teams to solve a problem.

Provide a specific example where cross-team collaboration was essential. Emphasize your role in facilitating discussions, sharing ideas, and how this collaboration led to a successful outcome.

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How do you tackle complex technical challenges as a Senior ML Ops Engineer?

Share your problem-solving strategy that includes breaking down challenges into manageable parts, leveraging team expertise, and continuously testing and iterating until you reach a solution.

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What experience do you have with AWS cloud services?

Detail your experience with specific AWS services relevant to the role, such as S3, EC2, or Lambda. Explain how you have utilized these services in past projects to enhance scalability or performance.

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How familiar are you with the machine learning lifecycle, and how does it impact your role?

Discuss your understanding of the machine learning lifecycle, noting any relevant tools you've worked with, and explain how this knowledge informs your work on deploying and monitoring ML models.

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What strategies do you use for mentoring junior engineers?

Describe your mentoring philosophy and strategies, such as regular one-on-one check-ins, providing constructive feedback, and encouraging knowledge sharing through coding sessions and team discussions.

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What do you think is the most important aspect of the Senior ML Ops Engineer's role in ensuring ethical AI practices?

Emphasize the importance of establishing monitoring systems that assess model performance against ethical standards, ensuring transparency, and recognizing biases while advocating for responsible AI use.

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Can you give an example of how you have integrated vendor technologies in your work?

Share an example where you successfully integrated vendor technologies such as Databricks or other cloud services, explaining the challenges encountered and the solutions implemented to achieve project goals.

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Skyscanner is a leading global travel search site, a place where people are inspired to plan and book direct from millions of travel options at the best prices. They are unbiased and free, which means that the 60 million people who use us every m...

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Full-time, hybrid
DATE POSTED
April 4, 2025

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