AI Machine Learning Engineer

As a trusted global transformation partner, Welocalize accelerates the global business journey by enabling brands and companies to reach, engage, and grow international audiences. Welocalize delivers multilingual content transformation services in translation, localization, and adaptation for over 250 languages with a growing network of over 400,000 in-country linguistic resources. Driving innovation in language services, Welocalize delivers high-quality training data transformation solutions for NLP-enabled machine learning by blending technology and human intelligence to collect, annotate, and evaluate all content types. Our team works across locations in North America, Europe, and Asia serving our global clients in the markets that matter to them. www.welocalize.com

To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

MAIN PURPOSE OF ROLE

The Machine Learning Engineer role is responsible for the design, development and implementation of machine learning solutions to serve our organization. This includes ownership or oversight of projects from conception to deployment with appropriate AWS services, Docker, MLFlow, and other. The role also includes responsibility for following best practices with which to optimize and measure the performance of our models and algorithms against business goals. MAIN DUTIES
The following is a non-exhaustive list of responsibilities and areas of ownership of the Machine Learning Engineer:
Design and develop machine learning models and algorithms for various aspects of the localization and business workflow processes, including machine translation, LLM finetuning, 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
Define and design solutions to machine learning problems. Integration with larger systems done with guidance of more-senior REQUIREMENTS
Education
Bachelor’s or Master’s degree in Computer Science, Mathematics, Machine Learning, Data Science or similar discipline (or equivalent experience)
Technical Skills and Experience
3+ years experience as a Machine Learning Engineer or similar role
Ability to write robust, production-grade code in Python
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 pls.
Experience with ML management technologies and deployment techniques, such as AWS ML offerings, Docker, GPU deployments, etc