One of the UK’s leading developers of artificial intelligence (AI) software. Its innovative AI platform is helping organisations to rapidly identify threat and opportunity signals buried within vast volumes of information so they can mitigate risk, act at speed, and gain a competitive edge.
With offices internationally and recent VC backing this company are looking to make huge strides across the Fintech space over the next few years, and having received a huge new round of funding they are looking to add a Lead Machine Learning Engineer to their roster.
With international offices and further plans for growth and international expansion through 2025 this is an opportunity not to be missed.
The Role:
In truth, I’m looking for a superhero here. You will be an experienced machine learning engineer with a focus on deploying deep learning models and graph neural networks to a production cloud environment. You will possess a strong computer science background and likely be MSc or PhD dedicated. This will be a hands-on role with day-to-day with project work focussed on deploying technical solutions to deliver a roadmap aligned with core product features.
Whether developing and implementing graph-based machine learning algorithms for a variety of use cases, including fraud detection, recommendation systems, and knowledge graphs. or collaborating with data scientists, software engineers, and domain experts to design and implement end-to-end machine learning pipelines, this is a role with a huge degree of variety.
The company operate a central London office and hybrid working is available.
The ideal candidate will possess the following skills and experience:
-Highly educated to at least a Masters level standard in a relevant or STEM-based subject.
– Strong background in machine learning and data science, including experience with graph algorithms and neural networks.
– Experience with graph-based machine learning frameworks, such as graph convolutional networks (GCNs), graph attention networks (GATs), or GraphSAGE.
-Experience deploying production-grade ML models.
– Familiarity with cloud computing platforms and distributed computing frameworks, such as AWS, Azure, or Hadoop.
– Knowledge of deep learning architectures, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), or Transformer models.
-An expert in not only developing algorithms but also applying them to real-world problems.
Job Owner: guy.williams
