Senior Machine Learning Engineer (RAGs)

Role overview

Design and deliver machine learning systems that combine information retrieval with large language model generation to address practical business needs. You will take RAG solutions from model development and evaluation through production deployment, partnering with client technical teams and leaders to shape useful AI applications.

Responsibilities

- Build and improve RAG systems, using algorithmic changes and model adaptation to raise their performance. - Fine-tune and adapt large language models for specific tasks and domains. - Work with data and engineering teams to establish the infrastructure needed for machine learning projects. - Collaborate with client leaders to identify promising AI and machine learning opportunities. - Deploy solutions with cross-functional teams and build applications such as search, chat, and summarization tools. - Write scalable, maintainable, documented, and testable code, with user needs in view.

Requirements

- At least five years developing and deploying machine learning models in production. - Four or more years working with production NLP and deep learning, using frameworks such as PyTorch or TensorFlow. - At least one year of experience with RAG or related techniques for improving model performance. - Strong Python skills and experience writing production-quality code. - Experience with large language models and frameworks such as LangChain, plus a solid grasp of prompting and the trade-offs between prompting and fine-tuning. - Experience with AWS, GCP, or comparable on-premises platforms; AWS is preferred.

Benefits and work setup

The role is fully remote and described as a complete engagement. Benefits include equity participation, paid time off, a company-wide winter break, an annual retreat, an education allowance, career roadmaps, and optional in-person meetups.