Senior AI Engineer in Computer Vision

As a **Senior AI Engineer** at Faktion, you will design, build, and deploy computer vision systems that solve real-world problems for our customers. The role combines hands-on machine learning with strong software engineering and MLOps practices. You will work across the full lifecycle of a machine learning system: exploring and improving datasets, developing and evaluating models, building training and inference pipelines, deploying models to production, and investigating performance issues once they are running in the field. A significant part of the role focuses on **computer vision for industrial applications**, including object detection, image classification, multispectral imagery, and real-time inference. You will also contribute to the platforms and tooling that allow our engineers to train, evaluate, deploy, and maintain machine learning models efficiently at scale. #### Key responsibilities * Develop, train, evaluate, and maintain deep learning models for computer vision tasks such as **object detection and image classification**. * Build and maintain **training and inference pipelines**, primarily using Azure Machine Learning. * Build data pipelines for processing large image datasets, including **multispectral and other multi-channel imagery**. * Explore and visualize datasets to identify **data quality issues, distribution shifts, labeling inconsistencies, and other factors that may affect model performance**. * Help define data collection, annotation, preprocessing, feature engineering, and augmentation strategies. * Work with annotation teams to define clear labeling guidelines and ensure training data is consistent and usable. * Train and deploy models that solve real-world problems on **industrial machines and production systems**. * Optimize models for the **latency, throughput, memory, and hardware constraints** of production environments. * Debug model, data, and pipeline issues in production and design strategies to improve performance. * Define appropriate **validation strategies, evaluation metrics, and test datasets** for machine learning systems. * Perform model error analysis and translate findings into improvements in data, modeling, or system design. * Prototype and evaluate new architectures, algorithms, and modeling approaches before integrating them into production. * Improve our shared ML platform and tooling, including **internal SDKs, data schemas, training pipelines, deployment tooling, and CI/CD**. * Review pull requests and help maintain strong engineering, testing, documentation, and code quality standards across the ML codebase. * Collaborate with machine learning engineers, software engineers, data engineers, and customer teams to design and deliver production-ready solutions. * Stay up to date with relevant developments in computer vision, deep learning, and MLOps and assess where new approaches can provide practical value.