What are the responsibilities and job description for the Machine Learning - Computer Vision Engineer position at CaseGuard?
We are seeking a seasoned Machine Learning Engineer specializing in Computer Vision to join our team with a solid background in training, experimenting and deploying machine learning and deep 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.
Key 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 the software engineers and product teams to identify opportunities for leveraging data.
- Collect, clean, and preprocess large datasets to prepare for model training and evaluation.
- Evaluate and optimize machine learning models for accuracy, performance, and scalability.
- Deploy efficient 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.
Required Qualifications:
- Bachelor's or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
- Strong programming skills in Python, C# and C .
- At least 4 years experience in leading projects of end to end modeling, deep learning training, and deployment using ML frameworks such as pytorch, Huggingface, Cuda, ONNX.
- Solid understanding of machine learning algorithms, including supervised and unsupervised learning and deep learning.
- Experience with data preparation and error analysis using various tools such as Pandas, NumPy, and SQL.
- Experience in MLOps principles and model deployment and instrumentation on cloud platforms such as AWS, Azure, or Google Cloud for model deployment and knowledge with efficient serving tools such as ONNX, triton, and vllm.
- Proficiency in working with image and video data, including preprocessing and augmentation techniques.
- Strong communication skills and the ability to work collaboratively in a team environment.
Great to have:
- Familiarity with containerization and orchestration tools like Docker and Kubernetes.
- Experience with version control systems such as Git.
- Understanding software engineering best practices, including code review, testing, and documentation.
- Experience with Large Language Models (LLMs) is a great to have.
- Experience with data annotation tools and processes.
Benefits:
- Competitive salary.
- Comprehensive health and wellness benefits.
- Professional development opportunities and continuous learning programs.
- Collaborative and inclusive work environment.
