Role Summary:
Working across projects within the operating company and the wider industry through our parent transport group, the ML Engineer will have responsibility for:
- Designing and implementing automation pipelines to operationalise the ML platform and ML pipelines for CI/CD Pipelines.
- Responsibility for mapping out data feeds into / out from the ML platform in collaboration with Solutions Architects and the IT Team.
- Ensuring that the ML systems are developed and scaled reliably in line with the best practices for ML Ops and software engineering, ethics, and system security.
- Creation of Data Engineering pipelines utilising Infrastructure as Code (IaC) to ensure projects ingest data in an automated way that is quality checked and fit for purpose for machine learning modelling.
Essential Skills:
- Proficient in Unix environment and scripting in Bash and Python.
- Strong experience with AWS infrastructure with proficiency in the services such as: S3, EC2, Lambda functions, Cloud Formation, Athena, Dynamo DB, Code Commit, SageMaker, etc.
- Strong experience with containerization using Docker and containers management.
- Strong software engineering skillset including code review: a good understanding of coding best practices and experience with code and data versioning (using Git/CodeCommit), code quality and optimisation, error handling, logging, monitoring, validation and alerting.
- Fluent in writing well tested, readable code that is capable of processing large volumes of data and large amount of data processing and ML jobs, including the use of IaC to build data pipelines.
- Expert knowledge of Python.
- An excellent knowledge of basic machine learning libraries, such as NumPy, SciPy, Pandas, Dask, PyTorch, Tensorflow, etc.
- A proven track record of linking data from multiple systems for scalable productionised solutions with security and monitoring best practices.
- Experienced with Cloud Security best practices.
- Hands-on experience with DevOps lifecycle, tools and frameworks.
Desirable Skills:
- Knowledge of ML approaches such supervised/unsupervised machine learning, reinforcement learning, Bayesian inference.
- AWS Certification is strong benefit.
- Experience with Google Cloud’s Big Data tools.
- Proficient with Kafka is a plus but not essential
