AI ML Research and Development Engineer

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Welo Global

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

🤖 Artificial Intelligence

🤝 B2B

☁️ SaaS

Artificial Intelligence • B2B • SaaS

Welo Global is a multilingual content, data, and AI solutions company that provides enterprise-focused localization, regulated translation, and AI training data through a family of brands (including Welocalize, Park IP, Welo Life Sciences, Welo Data, and Adapt). It combines domain expertise, proprietary AI tools (e. g. , the Opal suite), governed global workforces, and compliance certifications to support use cases across global marketing, intellectual property and legal translations, life sciences regulatory and clinical documentation, and AI model development. The company emphasizes scalable, AI-enabled platforms and workflows to deliver high-quality multilingual content and structured datasets in 300+ languages for corporate clients.

📋 Description

• 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

🎯 Requirements

• BSc in Computer Science, Mathematics or similar field; Master’s degree/PhD is a plus • Minimum 3+ years experience as an AI Machine Learning Engineer or similar role • 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 • 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 • Hands-on experience with AWS technologies including EC2, S3, and related deployment strategies • Experience with Docker; experience with GPU-based deployments is a plus • 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