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. Master's degree or PhD in

Computer Science, Artificial Intelligence, Machine Learning , or a related field, or equivalent professional experience. Several years of professional experience building machine learning systems, with a strong focus on

computer vision and deep learning . Strong

Python

programming skills. Hands‑on experience with

PyTorch and/or TensorFlow . Solid understanding of object detection and image classification, including

model architectures, loss functions, augmentation strategies, training techniques, and evaluation metrics . Experience working with common computer vision tooling and frameworks such as

OpenCV, YOLO‑based architectures, MMDetection, or similar ecosystems . Experience building and debugging

machine learning pipelines and models in production . Experience with a cloud ML platform such as

Azure Machine Learning, AWS SageMaker, or Google Vertex AI . Experience with Azure is a strong plus. Familiarity with

Docker, CI/CD, automated testing, versioning, monitoring, and other software engineering practices

for production ML systems. Experience optimizing models for

real‑time or high‑throughput inference , ideally on edge devices or production hardware. Strong analytical and problem‑solving skills, particularly when investigating complex interactions between data, models, and production systems. Comfortable taking ownership of shared code, tooling, and systems used by other engineers. Strong communication skills and the ability to collaborate effectively with both technical and non‑technical stakeholders. Nice to have:

Experience building or maintaining

MLOps platforms or shared ML infrastructure . Experience with

multispectral, hyperspectral, or other non‑standard imaging modalities . Experience deploying computer vision models on

edge devices, embedded hardware, GPUs, or industrial machines . Experience with mode