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 PURPOSE OF THE JOB
The AI 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, ML Flow, 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 TASKS & RESPONSIBILITIES

The following is a non-exhaustive list of responsibilities and areas of ownership of an AI/ML Research & Development 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

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.

REQUIREMENTS

Education

BSc in Computer Science, Mathematics or similar field; Master’s degree is a plus

Experience

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