CaseGuard is a leading technology company dedicated to developing innovative AI-powered solutions. We are committed to fostering a collaborative and inclusive work environment where employees can thrive and contribute to cutting-edge projects.

About the Position

Introduction

We are seeking a highly skilled and motivated Machine Learning Engineer specializing in Computer Vision to join our team at CaseGuard. The ideal candidate will have a strong background in developing and deploying machine learning models focused on image and video processing. You will work closely with cross-functional teams to design, implement, and optimize vision-based AI solutions to address real-world challenges.

Responsibilities

Design, develop, and deploy computer vision models for tasks such as object detection, object tracking, video segmentation, and facial recognition.
Optimize and fine-tune deep learning algorithms for real-time performance.
Work closely with software engineers and product teams to identify opportunities for leveraging data.
Collect, clean, and preprocess large datasets for model training and evaluation.
Evaluate and optimize machine learning models for accuracy, performance, and scalability.
Deploy models into production environments and monitor their performance to ensure reliability.
Stay up-to-date with the latest advancements in computer vision and artificial intelligence.
Collaborate with cross-functional teams to integrate machine learning solutions into business processes.
Document processes, models, and implementations to ensure reproducibility and scalability.

Requirements

Bachelor's or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
Experience in deep learning models, their training, and hyperparameter tuning using libraries such as TensorFlow, PyTorch, and Transformers or other Huggingface tools.
Experience with data manipulation tools such as Pandas, NumPy, and SQL.
Strong programming skills in Python and C++.
Experience in MLOps principles and model deployment and instrumentation on cloud platforms such as AWS, Azure, or Google Cloud. Knowledge of efficient serving tools such as ONNX, Triton, and vLLM.
Proficiency in working with image and video data, including preprocessing and augmentation techniques.
Strong understanding of machine learning algorithms, including supervised and unsupervised learning and deep learning.
Strong communication skills and the ability to work collaboratively in a team environment.

Nice to Have

Familiarity with containerization and orchestration tools like Docker and Kubernetes.
Experience with version control systems such as Git.
Understanding of software engineering best practices, including code review, testing, and documentation.
Experience with Large Language Models (LLMs).
Experience with data annotation tools and processes.