AI/ML Engineer
Company: FetchJobs.co
Location: Location not specified (Remote)
Type: Full-time
Remote: Yes
Posted: 2026-06-06
About this role
About The Company
RingCentral is a leading provider of cloud-based communications and collaboration solutions designed to empower businesses worldwide. Renowned for its innovative approach, RingCentral offers a comprehensive suite of products including voice, video, team messaging, and contact center solutions that facilitate seamless communication and enhance productivity. With a commitment to leveraging cutting-edge technology, RingCentral continuously invests in research and development to stay ahead in the dynamic digital landscape. The company's global presence and customer-centric philosophy have established it as a trusted partner for organizations seeking reliable and scalable communication platforms.
About The Role
We are looking for an experienced Lead AI/ML Engineer specializing in Natural Language Understanding (NLU) to join our innovative team at RingCentral. In this strategic role, you will spearhead the development of advanced conversational AI solutions that leverage large language models (LLMs), retrieval-augmented generation (RAG), and sophisticated prompt engineering techniques. Your expertise will be instrumental in designing scalable, reliable, and intelligent products that transform customer interactions and internal workflows. As a key leader in our AI/ML division, you will oversee the entire lifecycle of model development—from data ingestion and fine-tuning to deployment and ongoing optimization—ensuring that our AI solutions meet the highest standards of performance, safety, and fairness.
Qualifications
The ideal candidate will possess over 8 years of practical experience in natural language understanding and machine learning. A strong proficiency in Python and deep familiarity with frameworks such as PyTorch, Hugging Face Transformers, and Sentence Transformers are essential. You should have a proven track record of developing and deploying NLP or LLM pipelines at scale in production environments. A solid understandi...