Welo Global

AI ML Research& Development Engineer

Posted 23 Days Ago

Be an Early Applicant

2 Locations

Remote

Mid level

Artificial Intelligence • Machine Learning • Natural Language Processing • Professional Services

The Role

Design, develop, fine-tune, and deploy ML and NLP models (including MT and LLM finetuning). Own projects end-to-end using Python, TensorFlow/PyTorch, Docker, MLflow, and AWS; perform statistical analysis, monitor production models, document experiments, and collaborate across teams.

Summary Generated by Built In

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/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.
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/PhD is a plus

Experience

Minimum 3+ years experience as an AI Machine Learning Engineer or similar role
Skills & Knowledge
Core Engineering
Ability to write robust, production-grade code in Python
Strong foundation in machine learning techniques and algorithms, including supervised/unsupervised learning, deep learning, and reinforcement learning
Experience with NLP techniques and tools, including modern LLM-based approaches

LLM/Applied AI Tooling
Experience building with LLM orchestration frameworks (e.g., LangChain, LangGraph) or lightweight abstraction layers (e.g., LiteLLM)
Hands-on experience with the Hugging Face ecosystem
Experience working with managed agent platforms/sandboxes (e.g., Claude, Gemini, OpenAI)is a plus
Infrastructure & Deployment
Hands-on experience with AWS technologies including EC2, S3, and related deployment strategies
Experience with Docker; experience with GPU-based deployments is a plus
Leadership & Communication
Strong communication and collaboration skills, with the ability to explain complex technical concepts to non-technical stakeholders
Experience owning projects end-to-end from conception through deployment, and mentoring junior team members

When applying for this role please make sure that you are legally eligible to work in the country in which this job is based (Greece or Romania).

Skills Required

BSc in Computer Science, Mathematics or similar field
Master's degree
Minimum 3+ years experience as a Machine Learning Engineer or similar role
Production-grade Python coding experience
Strong knowledge of machine learning techniques (supervised, unsupervised, deep learning, reinforcement learning)
Hands-on experience with TensorFlow, PyTorch, and Scikit-learn
Experience with natural language processing (NLP) techniques and tools
Experience taking projects from conception to deployment and mentoring junior engineers
Hands-on AWS experience (Sagemaker, EC2, S3, SNS and other AWS ML offerings)
Experience with ML management and deployment technologies (Docker, MLflow, GPU deployments)
Excellent communication and documentation skills