AI ML Research& Development Engineer
🇬🇷 Greece | 🇷🇴 Romania
Python
Docker
AWS
Marketing
Machine Learning
Design
Legal
AI ML Research& Development Engineer
from 🇬🇷 Greece | 🇷🇴 Romania
About Welo GlobalÂ
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.comÂ
MAIN TASKS & RESPONSIBILITIES
The following is a non-exhaustive list ofresponsibilities and areas of ownership of an AI/MLÂ 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
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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 EngineeringAbility 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
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- LLM/Applied AI ToolingÂ
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 & DeploymentÂ
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 Â
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





