AI ML Research& Development Engineer - weloglobal

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/ML Engineer is responsible for the design, development, and deployment of machine learning solutions that serve our organization's business goals. This includes end-to-end ownership of projects from initial conception through production deployment, using AWS services, Docker, and modern ML/LLM tooling. The role also includes establishing and following best practices to optimize, monitor, and measure the performance of our models and algorithms against business outcomes.

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 systems for various aspects of the localization (translation) and business workflow processes

Take ownership of key projects from definition to deployment, ensuring that they meet technical requirements and maintain momentum and direction until delivery

Experiment with and evaluate classical ML and LLM-based approaches (including prompt/context engineering, retrieval-augmented generation, and agentic workflows) to identify effective solutions for business problems

Perform statistical analysis based on experimental and test results to drive measurable performance improvements

Package and deploy machine learning systems using appropriate techniques and technologies

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.