Deep Learning Engineer
| Dyddiad hysbysebu: | 31 Gorffennaf 2025 |
|---|---|
| Cyflog: | £54,423 i £59,898 bob blwyddyn |
| Oriau: | Llawn Amser |
| Dyddiad cau: | 30 Awst 2025 |
| Lleoliad: | B18 6BA |
| Gweithio o bell: | Hybrid - gweithio o bell hyd at 3 ddiwrnod yr wythnos |
| Cwmni: | DEVI TECHNOLOGIES LIMITED |
| Math o swydd: | Parhaol |
| Cyfeirnod swydd: |
Crynodeb
Design and develop deep learning models for tasks such as image classification, object detection, speech recognition, and natural language processing.
Train, evaluate, and optimize neural networks using large-scale datasets and advanced techniques like transfer learning, data augmentation, and hyperparameter tuning.
Implement state-of-the-art deep learning architectures, including CNNs, RNNs, LSTMs, Transformers, GANs, and autoencoders.
Collaborate with cross-functional teams including data scientists, software engineers, and product managers to define and deliver AI-powered features and solutions.
Build and maintain scalable data pipelines for preprocessing, labeling, and augmentation of structured and unstructured data.
Conduct research and literature reviews to evaluate and implement the latest algorithms and advancements in deep learning.
Optimize model performance for deployment using techniques like quantization, pruning, and model compression.
Deploy models into production environments, ensuring performance, reliability, and scalability using cloud platforms (AWS, GCP, Azure) or on-edge devices.
Write clean, maintainable, and well-documented code that adheres to software engineering best practices.
Monitor and troubleshoot deployed models, tracking key performance metrics and retraining when necessary.
Ensure ethical AI practices, including fairness, explainability, and accountability in model development and deployment.
Stay up-to-date with the AI/ML community, contributing to internal knowledge-sharing sessions and possibly publishing research papers or blog posts.
Train, evaluate, and optimize neural networks using large-scale datasets and advanced techniques like transfer learning, data augmentation, and hyperparameter tuning.
Implement state-of-the-art deep learning architectures, including CNNs, RNNs, LSTMs, Transformers, GANs, and autoencoders.
Collaborate with cross-functional teams including data scientists, software engineers, and product managers to define and deliver AI-powered features and solutions.
Build and maintain scalable data pipelines for preprocessing, labeling, and augmentation of structured and unstructured data.
Conduct research and literature reviews to evaluate and implement the latest algorithms and advancements in deep learning.
Optimize model performance for deployment using techniques like quantization, pruning, and model compression.
Deploy models into production environments, ensuring performance, reliability, and scalability using cloud platforms (AWS, GCP, Azure) or on-edge devices.
Write clean, maintainable, and well-documented code that adheres to software engineering best practices.
Monitor and troubleshoot deployed models, tracking key performance metrics and retraining when necessary.
Ensure ethical AI practices, including fairness, explainability, and accountability in model development and deployment.
Stay up-to-date with the AI/ML community, contributing to internal knowledge-sharing sessions and possibly publishing research papers or blog posts.
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Hyderus o ran Anabledd
Gwybodaeth am Hyderus o ran Anabledd
Yn gyffredinol, bydd cyflogwr Hyderus o ran Anabledd yn cynnig cyfweliad i unrhyw ymgeisydd sy'n datgan eu bod yn anabl ac yn bodloni'r meini prawf lleiaf ar gyfer y swydd fel y diffinnir gan y cyflogwr. Mae'n bwysig nodi, mewn rhai sefyllfaoedd recriwtio fel nifer fawr o ymgeiswyr, cyfnod tymhorol ac amseroedd prysur iawn, efallai y bydd y cyflogwr am gyfyngu ar y niferoedd cyffredinol o gyfweliadau a gynigir i bobl anabl a phobl nad ydynt yn anabl. Am fwy o fanylion ewch i Hyderus o ran Anabledd.