Senior AI Engineer Computer Vision

G10X Pte Ltd; Greater London; Vor Ort; GBP 70.000 - 110.000; Vollzeit

Zusammenfassung

G10X Pte Ltd is seeking an experienced machine learning engineer to design and deploy scalable ML systems for demand forecasting. You will architect end-to-end pipelines, work with PySpark and Databricks, and build multimodal models.

You will collaborate with scientists and engineers, translate research into production, focus on reliability, observability, and cloud/MLOps practices.

Qualifikationen

• Significant experience designing and deploying ML systems.
• Strong Python production-quality software engineering.
• Extensive PySpark and Databricks experience.
• End-to-end ML pipelines from data prep to monitoring.
• Experience with PyTorch or TensorFlow.
• Multimodal or embedding models background.
• Transformer and foundation model familiarity.
• Distributed computing with large datasets.
• Cloud platforms and MLOps experience.
• Strong communication and collaboration.
• Pragmatic mindset balancing tech and business value.

Aufgaben

• Design, develop, and deploy scalable ML systems for demand forecasting.
• Maintain ML workflows across model lifecycle: data prep, training, inference, monitoring.
• Build large-scale data pipelines with PySpark and Databricks.
• Develop multimodal ML solutions combining images, text, metadata, and signals.
• Train and productionise deep learning models (Transformers, foundation models).
• Collaborate with Applied Scientists to productionize models.
• Improve performance, reliability, and observability of production ML systems.
• Foster engineering excellence through reviews, mentoring, and knowledge sharing.

Kenntnisse

ML system design

Python programming

PySpark

Databricks

Multimodal ML

Transformers

Distributed computing

Cloud + MLOps

Communication

Pragmatic mindset

Tools

PyTorch

TensorFlow

Databricks

Spark

Jobbeschreibung

• Design, develop, and deploy scalable machine learning systems that power demand forecasting and related AI products.
• Design and maintain reliable ML workflows across the model lifecycle, including data preparation, experimentation, training, evaluation, deployment, inference, monitoring, and continuous improvement.
• Build and optimise large-scale data processing and feature engineering pipelines using technologies such as PySpark and Databricks, enabling efficient preparation of training and inference datasets.
• Develop multimodal machine learning solutions that combine diverse data sources, including product images, text, structured metadata, and behavioural signals, to create rich representations for downstream AI applications.
• Train and productionise deep learning models using modern architectures such as Transformers, foundation models, and other representation learning approaches.
• Partner with Applied Scientists to translate new modelling approaches into reliable, scalable production systems.
• Improve the performance, reliability, scalability, and observability of ML systems operating in production.
• Drive engineering excellence through architecture discussions, code reviews, mentoring, and knowledge sharing.

About You

You'll likely have:

• Significant experience designing and deploying machine learning systems in production environments.
• Strong software engineering skills in Python, with experience building maintainable, tested, and production-quality code.
• Strong experience with large-scale data processing using technologies such as PySpark and Databricks.
• Experience designing and building ML pipelines across the full lifecycle, from data preparation and model development through to deployment and monitoring.
• Experience developing deep learning models using frameworks such as PyTorch, TensorFlow, or similar.
• Experience with multimodal machine learning, representation learning, or embedding models, combining data sources such as images, text, structured metadata, or behavioural signals.
• Strong understanding of modern deep learning architectures, particularly Transformers, foundation models, multimodal learning, and representation learning techniques.
• Experience working with distributed computing, large datasets, and scalable model training or inference systems.
• Familiarity with cloud platforms and modern MLOps practices.
• Strong communication skills and the ability to collaborate effectively with scientists and engineers.
• A pragmatic mindset, balancing technical excellence with delivering business value.