responsible for building and maintaining our labelling, training and production inference data pipelines to produce high quality datasets, models and services to power our automated vehicle inspection product. Following MLOps and DevOps best practices you will build and deploy bespoke computer vision ML models using a service-oriented architecture in AWS, GCP and on Edge to process photos from DeGould … other tasks/duties as may be reasonably required to fulfil DeGould's objectives. Depending on the individual role, some or all of the following: Developing and championing robust MLOps frameworks and policies. Training and maintaining performant vehicle segmentation models. Labelling tasks and data quality. Designing and implementing reporting dashboards. Developing novel approaches from academic and industry research. Production model … of at least one of PyTorch, Keras, or Tensorflow. Working knowledge of core AWS concepts and services such as EC2, ECS, EKS, and DynamoDB. Good knowledge of DevOps and MLOps tools, including usage of Git, Bash, UNIX, Docker, containers and CI/CD pipelines (GitHub Actions or similar). Able to work effectively both as part of a team and More ❯
Familiarity with AWS and its tools Excellent communication skills and a collaborative mindset Nice to have: Experience with AI/ML infrastructure or supporting data science workflows Exposure to MLOps tooling or machine learning model deployment More ❯
data. Collaborate with legal SMEs to translate domain knowledge into scalable machine learning solutions. Continuously evaluate model performance, ensuring accuracy, fairness, and compliance. Help shape the data pipeline and MLOps practices for handling sensitive legal content securely. Required Experience: Solid experience with Python and ML/NLP libraries (e.g., spaCy, Hugging Face, TensorFlow/PyTorch). Experience building NLP or More ❯
data. Collaborate with legal SMEs to translate domain knowledge into scalable machine learning solutions. Continuously evaluate model performance, ensuring accuracy, fairness, and compliance. Help shape the data pipeline and MLOps practices for handling sensitive legal content securely. Required Experience: Solid experience with Python and ML/NLP libraries (e.g., spaCy, Hugging Face, TensorFlow/PyTorch). Experience building NLP or More ❯
independently in a hybrid or remote environment. Experience working in agile environments and iterating based on user feedback. Preferred Skills: Background in deploying AI/ML models to production (MLOps). Experience using cloud platforms such as Azure. Interest or experience in building autonomous agents or internal AI assistants. Why Join? Competitive salary, pension and attractive benefits package. Work on More ❯
