MLOps Data Engineer

• Work in agile pods to design and build cloud hosted ML products with automated pipelines that run monitor and retrain ML Models
  
• Design AI/ML apps and implement automated model and pipeline adaption and validation working closely with data scientists and data engineers
  
• Support life cycle management of deployed ML apps (e.g. new releases change management monitoring and troubleshooting)
  
• Work as MLOps subject matter expert (e.g. develop and maintain enterprise standards user guides release notes FAQs)
  
• Build processes supporting seamless MLOps (e.g. app monitoring troubleshooting life cycle management and customer support)
  
• Walk stakeholders and solution partners through solutions and reviewing product change and development needs
  
• Maintain effective relationships with app userbase to develop education and communication content as per life cycle events
  
• Researching and gain expertise on emerging tools and technologies
  
• An enthusiasm to ask questions and try and learn new things is essential
  

Requirements

• Experience in data science statistics software engineering modular design and design thinking
  
• Experience developing CI/CD pipelines for AI/ML development deploying models to production and managing the lifecycle in a regulated environment
  
• Experience building and deploying data science apps with large scale data and ML pipelines and architectures
  
• Experience working in an agile pod supporting and working with crossfunctional teams
  
• Good understanding of ML and AI concepts and handson experience in development deployment and agile life cycle management of data science apps (MLOps)
  
• Ability to assess new technologies and compile architecture decision records (ADRs)
  
• Excellent communication skills in English both verbal and in writing
  
• Graduate degree in Computer Science Information Systems Software Engineering or another quantitative field
  
• Ability to work across the full stack and move fluidly between programming languages and MLOps technologies (e.g. Python Spark R Metaflow Github MLFlow Argo)
  
• Experience in cloud and highperformance computing environments
  
• Experience in AWS (e.g. S3 Lambda EC2 cloud watch) and other similar technologies (e.g. ELK stack Snowflake Informatica)
  
• Knowledge of relational databases query authoring (SQL) and designing variety of databases (e.g. Postgres SQL Document store)
  
• Nice to have knowledge of visualization technologies (e.g. RShiny Python DASH Tableau PowerBI web framework)
  
• Experience in development deployment and operations of AI/ML modelling of complex datasets
  
• Experience in developing and maintaining APIs (e.g. REST)
  
• Experience specifying infrastructure and Infrastructure as a code (e.g. docker Kubernetes EKS Terraform)
  
• Experience in cloudbased ML engineering in an industrial setting within a global organization (technology company preferred)
  
• Experience on working within compliance (e.g. quality regulatory data privacy GxP SOX) and cybersecurity requirements is a plus
  
• For more senior roles mentoring and/or technology evangelism/advocacy experience