Machine Learning Ops Data Engineer

Join us as a Machine Learning Ops Data Engineer

This is an exciting opportunity to use your technical expertise to collaborate with colleagues and build effortless, digital first customer experiences
You'll be simplifying the bank through developing innovative data driven solutions, inspiring to be commercially successful through insight, and keeping our customers and the bank safe and secure
Participating actively in the data engineering and science communities, you'll deliver opportunities to support our strategic direction while building your network across the bank

What you'll do
You'll drive value for the customer through modelling, sourcing and data transformation. You'll be working closely with core technology, architecture, and Data Science teams to deliver strategic ML and AI Models, while driving Agile and DevOps adoption in the delivery of ML solutions.
We'll also expect you to be:


Delivering the automation of data engineering pipelines and ML processes to support the deployment of data science cases through the removal of manual stages
Deployment of efficient, elegant solutions to challenges associated with ML models in productions such as retraining strategies, data drift & model monitoring triggers
Conducting common and specialized data monitoring and ML/AI model performance analysis in production
Supporting best practice for development of data and ML pipelines, as well as the creation of reusable code and data assets
Embedding new data techniques into the business through role modelling, training, and experiment design oversight

The skills you'll need
To be successful in this role, you'll need to be an intermediate level programmer with experience of building machine learning pipelines. Ideally, you will have a qualification in Computer Science, Software Engineering, or a relevant quantitative discipline. A critical thinker with sound problem-solving abilities, you'll have experience in extracting value and features from large scale data.
A critical thinker with sound problem-solving abilities, also need a good understanding of Data Science techniques, including using data to drive insights and applying statistical and machine learning models. Finally, you should have worked on the deployment of solutions in production environments on-prem or in the cloud.
You'll need to demonstrate:


Experience using RDMS, Hadoop and SQL
Experience in deploying highly scalable and reliable data or ML pipelines using BigData techniques and tools
Exposure to shipping scalable solutions in the cloud like AWS, Azure, and GCP
Software development experience with strong coding skills in Python, Java, Scala or another language, applying best practices for the full development life cycle
Experience of ETL Quality Assurance and documentation
Experience or strong understanding of DevOps & MLOps practice and principles

It will also be beneficial if you have experience building CI/CD pipelines and tools like Jenkins and TeamCity. Experience of Unix scripting, working with NoSQL databases and working with pub/sub and event driven technologies like Kafka is also a plus.
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