Platform Engineer (MLOps Platforms) 

• You love building customer focussed solutions 

• We are a team of big thinkers, who love to push boundaries and create new solution 

• Together we will build tomorrow’s bank today, using world-leading technology and innovation 

Do work that matters: 

The role of Platform Engineer is to Design, Build, Run & Evolve tools, infrastructure, templates and capabilities that our data science community and other engineers use to deliver business value, and to write code that automates running our infrastructure and environments. Help to make our platforms loved by our engineers and data science community!   We are keen hear from ML platform engineers who are passionate about infrastructure as code, software eating the world, LLMs, GPUs and High-Performance Compute! 

We support our people with the flexibility to balance where work is done with at least half their time each month connecting in office. We also have many other flexible working options available including changing start and finish times, part-time arrangements and job share to name a few. Talk to us about how these arrangements might work in the role you’re interested in. 

We’re interested in hearing from people who:

• As a Platform Engineer is experienced in the Full Cycle model, where engineers are involved in Design, Build, Change, and Run

• Have a passion for designing, developing, deploying and running high quality modern machine learning platforms

• Contributes to a culture where quality, inclusiveness and excellence are championed

• Have a natural drive to educate, communicate and positively influence various stakeholder groups including high level executives

Roles and Responsibilities of a Platform Engineer on the MLOps Platform:   

• Collaboratively work with customer facing product owners and engineers to design, build and run platforms that they can use to deliver customer value at greater quality, velocity, and safety

• Write code that automates the operations of our platforms, infrastructure, environments, controls implementation and tooling

• Maintain the underlying infrastructure to ensure the that it is reliable, secure and scalable

• Make all platforms entirely self-service, secure, and available within minutes without human approval

• Collaborate with data scientists, engineers and stakeholders to implement technical requirements. Translate needs into technical solutions and ensure the platform's reliability through robust monitoring, logging, and alerting systems

• Develop and maintain comprehensive documentation, including architecture blueprints and best practices as well as conduct workshops and training sessions to educate and align the team on platform usage and best practices 

• Stay up to date with the latest development in the field of ML, MLOps, LLMs, GPUs and related concepts  

Tech Skills:  

We use a broad range of tools, languages, and frameworks. We don’t expect you to know them all but experience or exposure with some of these (or equivalents) will set you up for success in this team;   

 AWS Services: Working knowledge of AWS services such as EC2, ECS, S3, Lambda, Step function, RDS, DynamoDB, IAM, VPC, Route 53, Cloudwatch, EKS  

ML Services (Good to have): Experience in AWS ML services like SageMaker, AWS Glue, Amazon EMR. Familiarity with AWS Bedrock, Amazon Q services, NVIDIA GPUs and related frameworks, LLMs. 

 Model Lifecycle (Good to have): Experience with the end-to-end ML lifecycle, including data preprocessing, feature engineering, model training, evaluation, and deployment 

 Scripting: Proficient in automation and Scripting (Bash, Python). 

 IaC Tools: Hands-on experience with infrastructure as code tools like AWS CloudFormation, 

Version Control: Proficiency with version control systems like Github, Github Actions