ML OPs Engineer

Luton/Hybrid  

COMPANY

When it comes to innovation and achievement there are few organisations with a better track record. Join us and you’ll be able to play a big part in the success of our highly successful, fast-paced business that opens up Europe so people can exercise their get-up-and-go. With over 300 aircraft flying over 800 routes to more than 30 countries, we’re the UK’s largest airline, the second largest in Europe and the tenth largest in the world. Flying over 80 million passengers a year, we employ over 13,000 people. Its big-scale stuff and we’re still growing.

 JOB PURPOSE

The ML Ops Engineer acts as the backbone of machine learning operations, bridging the gap between research and production. They collaborate with teams across the organisation to ensure that machine learning models are scalable, reliable, and seamlessly integrated into business processes. This role is critical for driving innovation while enabling teams to focus on strategic business challenges.

JOB ACCOUNTABILITIES

To shape and manage future direction of easyJet’s Data Technology & Product architecture, with a primary focus on our enterprise-scope Data domains, by:

·       Manages the technical aspects of machine learning operations, ensuring workflow robustness, scalability, and reliability. Designs and maintains CI/CD pipelines for continuous integration, deployment, and monitoring of machine learning models in production.

·       Automates machine learning pipelines for efficient data flow and transformation, ensuring high data quality. This includes tools for data versioning, lineage tracking, and model reproducibility.

·       Integrates machine learning models into production by fostering collaboration between data scientists, engineers, and analysts to align technical solutions with business goals.

·       Ensures model lifecycle management, covering training, deployment, monitoring, and retraining to adapt to new data. Optimises deployment pipelines for scalability and efficiency using cloud platforms, Docker, Kubernetes, and workflow orchestration tools.

·       Implements best practices for production model monitoring, logging, and alerting to ensure the reliability of deployed machine learning systems. They also use statistical process control to identify and troubleshoot issues related to model drift and pipeline failures. 

·       Collaborates with cross-functional teams to ensure smooth handoffs of machine learning solutions. They help data scientists transition research models into production-ready solutions and work with engineers to maintain optimal performance in production environments.  

KEY SKILLS REQUIRED

To excel as a Machine Learning Operations (MLOps) Engineer, candidates must possess a unique blend of technical expertise, problem-solving capabilities, and collaborative skills. This role requires proficiency in managing machine learning pipelines, integrating models into production environments, and ensuring the scalability, reliability, and efficiency of deployed systems.

By bridging the gap between research and production, the MLOps Engineer plays a critical role in driving innovation and aligning technical solutions with strategic business objectives. 

·       Proficiency in designing, implementing, and managing CI/CD pipelines for seamless integration, deployment, and monitoring of machine learning models.

·       Strong understanding of automating machine learning pipelines, including data versioning, lineage tracking, and reproducibility, to ensure high data quality and transformation efficiency.

·       Skill in integrating machine learning models into production environments by collaborating with data scientists, engineers, and analysts to align technical solutions with business objectives.

·       Ability to manage the end-to-end model lifecycle, including training, deployment, monitoring, and retraining, with a focus on scalability and efficiency.

·       Expertise in implementing best practices for monitoring and logging production models, troubleshooting data drift and pipeline failures, and ensuring system reliability.

·       Experience in working closely with cross-functional teams to transition research models into production and maintaining optimal performance in production environments.

·       Ability to collaborate with Data Engineers, Platform Engineers, and Analytical Engineers to design and maintain scalable, efficient pipelines while ensuring smooth data transformation processes.

·       Skill in validating data outputs, defining reporting requirements, and establishing feedback loops to enhance usability and dashboard accuracy.

·       Familiarity with cloud platforms, containerisation technologies, and workflow orchestration tools such as Kubernetes and Docker for deploying scalable machine learning systems.

What you’ll get in return 

·Competitive base salary 

·Up to 30% bonus 

·25 days holiday 

·BAYE, SAYE & Performance share schemes 

·7% pension 

·Life Insurance

·Work Away Scheme

·Flexible benefits package 

·Excellent staff travel benefits 

About easyJet

At easyJet our aim is to make low-cost travel easy – connecting people to what they value using Europe’s best airline network, great value fares, and friendly service.

It takes a real team effort to carry over 90 million passengers a year across 35 countries. Whether you’re working as part of our front-line operations or in our corporate functions, you’ll find people that are positive, inclusive, ready to take on a challenge, and that have your back. We call that our ‘Orange Spirit’, and we hope you’ll share that too.

Apply

Complete your application on our careers site.

We encourage individuality, empower our people to seize the initiative, and never stop learning. We see people first and foremost for their performance and potential and we are committed to building a diverse and inclusive organisation that supports the needs of all. As such we will make reasonable adjustments at interview through to employment for our candidates.