ML Sr. engineer

17 Apple Lane
Exeter
EX2 5GL
United Kingdom
Overview
DeGould is looking for an outstanding Machine Learning Engineer to work alongside our internal Product Team on our Spec Check product. We would like this person to:
- Sit alongside some of our best ML team members and leads over the next 2-3 months and work with them to fi x the product .
- Lend valuable insight and advice on architecture and approach.
- In the course of this work, bring in their insight on how we can build a scalable delivery team that will be responsible from Q2 for monitoring, evaluating and training Spec Check models. In the longer term this team would also work on Defect Detection models.
- Build an innate understanding of DeGould s ML products and how they re delivered to customers.
- From Q2 they will be our lead external engineer with our partner, working with an internal ML leader at DeGould to run the delivery team.
About Degould
DeGould is an exciting, multi-award-winning company in the software and AI sector with a range of blue chip corporate clients (including Toyota, Jaguar Land Rover, Daimler and Bentley), implementing innovative vision and damage detection systems. As the company embarks on an exciting growth phase, the company plans to expand its team, further develop existing products, and explore opportunities for new ones.
The Candidate
The ideal candidate will have a comprehensive understanding of how to build and deploy bespoke computer vision models to a service-led architecture in AWS. You will be able to be responsible for end-to-end model pipelines, from building upon pre-existing image data and labelling pipelines to produce high quality datasets, to monitoring and setting up automated retraining pipelines using best practices in MLOps and DevSecOps.
The ideal candidate will be a strong problem solver combined with an eye for detail, have high standards for quality, and a real desire to own entire aspects of a pipeline that is central to our core business value chain.
The successful candidate will be a team player, working with a team of over 8 similarly-minded ML engineers and data scientists, and experience and desire to manage a small team is preferable.
To achieve this, Python and AWS skills are necessary along with the ability to build empathy with our customer s requirements and our colleagues.
Responsibilities
Create production ML pipelines for images from our proprietary production line imaging booths.
17 Apple Lane
Exeter
EX2 5GL
United Kingdom
Develop infrastructure and tools for rapid machine learning experimentation.
Implement and develop new Deep Learning approaches and services in AWS.
Continuous evaluation and improvement of models in production.
Create and maintain high-quality code standards to solve interesting and highly impactful problems.
Stay up to date with the latest tools, research, methods and technologies, and keep an eye open for opportunities to apply these.
Manage and support small teams of product-focused ML Engineers and Data Scientists.
Work across multidisciplinary teams to deliver against the company s objectives.
Essential Skills
Expertise across ML Pipeline and production MLOps.
Technical expertise in AI for image processing using: Deep Learning, Machine Learning, Transfer Learning, CNN architectures and the benefi ts of each (Yolov4, RCNN, ResNet etc).
Knowledge of relevant metrics and how to apply them to monitor model performance.
Strong profi ciency with Python, and preferably two of Tensorfl ow, PyTorch, Keras, Nvidia TAO (TLT).
Experience in data visualisation tools such as Matplotlib, Plotly, Streamlit, AWS Quicksight etc.
Ability to write high quality, readable code with a focus on maintainability.
Ability to query relational databases using SQL.
Strong experience with DevOps and MLOps tools e.g. git, Docker, MLFlow, Flyte, SageMaker etc.
Strong experience with CI/CD e.g. Github workfl ows, Circle CI etc.
Strong familiarity with AWS services, including S3, SageMaker, EC2, ECS, RDS and more. Desirable Skills
Desirable: Experience with labelling tools such as Labelbox, CVAT, AWS Groundtruth.
Experience with Kubernetes.
Experience with designing data pipelines and feature stores.
Management experience would be a plus.