About Welo Global
Welo Global is a leader in multilingual AI, technology, and content solutions serving over 2,000 clients in 300 languages. The company combines globally scaled multilingual infrastructure, including a network of over 500,000 linguists and domain experts, with advanced NLP, computational linguistics, and best-in-class compliance backed by seven ISO certifications. Welo Global’s five brands—Welocalize (multilingual content and localization services for global enterprises), Park IP (intellectual property and patent translation services for law firms and corporate legal teams), Welo Life Sciences (regulated language and compliance-aligned content solutions for pharmaceutical, biotech, and medical device organizations), Adapt (multilingual performance-led digital marketing agency), and Welo Data (multilingual data generation, evaluation, and human data infrastructure for AI systems)—serve distinct customer segments with purpose-built expertise, fit-for-purpose solutions, and supporting technology. weloglobal.com
MAIN TASKS & RESPONSIBILITIES
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
Project scope: Define and design solutions to machine learning problems. Integration with larger systems done with the guidance of more senior engineers.
Success Indicators for a Machine Learning Engineer
Effective Model Development: Success is evident when the models developed are accurate, efficient, and align with project requirements.
Positive Team Collaboration: Demonstrated ability to collaborate effectively with various teams and stakeholders, contributing positively to project outcomes.
Continuous Learning and Improvement: A commitment to continuous learning and applying new techniques to improve existing models and processes.
Clear Communication: Ability to articulate findings, challenges, and insights to a range of stakeholders, ensuring understanding and appropriate action.
Ethical and Responsible AI Development: Adherence to ethical AI practices, ensuring models are fair, unbiased, and responsible.
The following is a non-exhaustive list of responsibilities and areas of ownership of an AI/ML Research & Development Engineer
REQUIREMENTS
BSc in Computer Science, Mathematics or similar field; Master’s degree is a plus
Minimum 3+ years experience as a Machine Learning Engineer or similar role
Skills & Knowledge
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
Education
Experience
