AI Machine Learning Engineer

April 8

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translation • localization • globalization • technology • machine translation

1001 - 5000

Description

• Design and develop machine learning models and algorithms for various aspects of the localization and business workflow processes, including machine translation, LLM fine tuning, and quality assurance. • Take ownership of key projects from definition to deployment, ensuring that they meet technical requirements and maintain momentum and direction until delivery. • Evaluate and select appropriate machine-learning techniques and algorithms to solve specific problems. • Implement and optimize machine learning models and technologies using Python, TensorFlow, and other relevant tools and frameworks. • Perform statistical analysis and fine-tuning using test results. • Deploy machine learning models and algorithms using appropriate techniques and technologies, such as containerization using Docker and deployment to cloud infrastructure. • Use AWS technologies (including but not limited to Sagemaker, EC2, S3) to deploy and monitor production environments. • Keep abreast of developments in the field, with a dedication to learning in the role. • Document diligently and communicate thoughtfully about ML experimentation, design, and deployment. • Project scope: Define and design solutions to machine learning problems. Integration with larger systems done with guidance of more-senior.

Requirements

• Excellent communication and documentation skills. • Strong knowledge of machine learning techniques and algorithms, including supervised and unsupervised learning, deep learning, and reinforcement learning. • Hands-on, high proficiency experience with machine learning frameworks such as TensorFlow, PyTorch, and Scikit-learn. • Experience with natural language processing (NLP) techniques and tools. • Strong communication and collaboration skills, with the ability to explain complex technical concepts to non-technical stakeholders. • Experience taking ownership of projects from conception to deployment, and mentoring more junior team members. • Hands-on experience with AWS technologies including EC2, S3, and other deployment strategies. Experience with SNS, Sagemaker a plus. • Experience with ML management technologies and deployment techniques, such as AWS ML offerings, Docker, GPU deployments, etc.