Staff Platform Engineer (MLOps Platforms) 

Staff Platform Engineer (MLOps Platforms) 

• You are highly experienced in 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. Collaboratively work with customer facing product owners and platform engineers to design, build and run ‘platforms’ that they can use to deliver customer value at greater quality, velocity, and safety. 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 Staff Platform Engineer is expert at 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 Staff Platform Engineer on the MLOps Platform:  

• Provide strategic technical leadership and mentorship driving best practices for ML platform architecture, deployment and scaling

• Oversee the design and development of scalable and resilient ML infrastructure with a focus on performance and reliability and architect core components, ensuring performance, reliability, and scalability

• Lead the development of tools and frameworks to streamline the ML lifecycle, from data ingestion to model deployment and monitoring, understanding DevSecOps frameworks, interact with vendors and understand their product roadmap

• Create a standardised set of tooling for deploying and running applications and setting them up with best practices

• Participate in cross-group activities to build a culture of one team, bar-raising both our engineering capability and our technology solutions to drive our strategy

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

• Drive Implementation of CI/CD pipelines to streamline ML model deployment and updates; Troubleshoot complex technical issues to minimize disruptions

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

ML Services: Expertise 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: 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 

Monitoring & Observability: Expertise in tools like Grafana, Prometheus 

Engineering Tooling: Artifactory, Synk, Docker