Work in a cross-functional team alongside data scientists and machine learning scientists
Implement and deploy cutting-edge machine learning models for measurable impact across the business
Create the tools, frameworks, and libraries that enable the acceleration of our ML product delivery
Drive improvements to our current AI workflows in terms of process, performance, and testing
Mentor and coach team members on ML engineering best practices
Build capabilities for our MLOps platform
Job Requirements:
Bachelor s/Master s degree in Engineering, Computer Science, Mathematics or a similar quantitative discipline
At least 3+ years of relevant experience as a software engineer
Hands on experience in delivering machine learning models to production at scale
Prior experience in writing production-quality Python code
Comfortable working with Python data science and machine learning libraries with a particular focus on deep learning frameworks such as scikit-learn, TensorFlow, Keras, pandas, numpy, PyTorch, XGBoost
Strong understanding of machine learning applications development life cycle processes and tools: CI/CD, version control (git), testing frameworks, MLOps, agile methodologies, monitoring and alerting
Comfortable working with Docker and containerised applications
Operating and supporting Kubernetes clusters at scale
Nice to have some knowledge of DevOps and Google Cloud Platform (GCP)
Experience accessing and combining data from multiple sources and building data pipelines (ETL)
Ability to work collaboratively and proactively in a fast-paced environment alongside scientists, engineers and non-technical stakeholders
Ability to combine business intuition with the application of advanced solutions
A passion for keeping up with the latest ongoings in Data Science and Machine Learning communities
A curious mind, self-starter and endlessly keen to learn and develop themselves professionally
Fluent in spoken and written English communication