Cornucopia has partnered up with a rapidly growing financial company, who are now looking to expand their Computer Vision Engineer team. The role would entail leading our in house and client facing automation/machine learning initiatives, independently or within a team, you will be working independently at first. You will be leading the optimisation and development of our existing computer vision model that detects damage on vehicles and provides an output with an actionable recommendation. You will also be challenged with leading new in house and client facing projects, from operation optimisation, automated development to forecasting insurance related catastrophise and remote property assessment. You must be self-managed and be able to effectively communicate and solve business problems. You are required to deliver commercially viable and usable models, this is not a research focused opportunity. Responsibilities Design and develop in-house R-CNN Model for vehicle damage detection. Design and optimise our predictive property assessment model. Research, develop, evaluate and optimize our model(s). Solve problems independently. To create architecture designs Create API and end points for inference function of models Work with stakeholders within our company to create use cases for new initiatives. Deliver commercially viable and useable ML & Ai models. Requirements A minimum of 4 years of dedicated Computer Vision Must be able to deliver useable models and be able to scale from research to commercial implementation. Strong experience in Python, numpy, pandas, SQL, C++ Experience working in a cloud environment (AWS, Azure, GCP) or a containerized environment (Mesos, Kubernetes) Deep knowledge on TensorFlow, Pytorch, Keras, SparkML. Mathematically strong in statistics and foundations of AI and understand neural networks. Good understanding of the complexity for engineering real-world AI/ML applications such as prediction, recommendation, computer vision, bots, NLP, sentiment, knowledge and content intelligence, etc. Application Domains of ML: Natural Language Processing (NLP), Text-based NLP, Emotion- or Accent-detection in language, Vision-based (Image segmentation, classification, scene analysis), Recommendation Engines, Planning in Search, Semantic Engines. Self-starter, ability to identify issues, hold self and others accountable to achieve goals and resolve problems. Good command of written and spoken English.