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.