Position Overview

We are seeking a dedicated MLOps Engineer to join our R&D team, comprised of five innovative developers and engineers, focusing on the operational aspects of machine learning. This role is pivotal in developing, deploying, and maintaining scalable and efficient AI models, particularly for vision-related applications on edge devices. The ideal candidate will have a robust understanding of both machine learning algorithms and software engineering principles, emphasizing the automation of machine learning model lifecycle management, from development to production.

Minimum Qualifications

• Senior-level M.Sc. or PhD in Computer Science, Engineering, Data Science, AI, Machine Learning, or related technical fields.

• Demonstrated experience in MLOps or related field, with a proven track record of deploying and maintaining AI models in production environments.

• Strong knowledge of machine learning algorithms, data pipeline architectures, and model deployment strategies.

• Proficient in Python and at least one other programming language (e.g., C/C++, Java, Scala, Kotlin, Swift, Dart).

• Experience with machine learning frameworks (e.g., ONNX, TensorFlow, PyTorch) and understanding their deployment mechanisms on edge devices.

• Familiarity with version control systems (e.g., Git) and automation tools (e.g., Jenkins, GitLab CI/CD).

• Excellent analytical, problem-solving, and communication skills, capable of working collaboratively across teams.

• Experience with one or more ML tools, such as Kubeflow, MLflow, Metaflow, BentoML, AWS SageMaker.

Preferred Qualifications

• Practical experience with deploying AI models on edge devices, including optimization for low-power and limited-resource environments.

• Experience with mobile development environments and frameworks.

• Knowledge of cloud computing services and the integration of edge computing with cloud-based architectures.

• Understanding of monitoring and logging tools for maintaining high availability and performance of AI applications in production.

• Familiarity with data encryption and secure model deployment practices to ensure data privacy and security on edge devices.