Senior Machine Learning Engineer – RAGs

This is a fully remote position, open to applicants in Latin America.

📋 Description

• Design, develop, and enhance Retrieval-Augmented Generation (RAG) models to address complex, real-world challenges faced by clients.

• Elevate RAG model efficiency through cutting-edge algorithms, innovative methodologies, and meticulous fine-tuning.

• Collaborate with client Data and Engineering teams to construct a robust machine learning infrastructure.

• Partner with client leadership to identify and exploit transformative AI/ML opportunities.

• Fine-tune and customize large language models for targeted tasks and domains within the RAG framework.

• Deploy RAG models in production settings, ensuring seamless integration and sustained success.

• Utilize advanced machine learning techniques, including LLMs, to create AI solutions tailored to client needs.

• Produce clean, maintainable, scalable, documented, and testable code.

• Emphasize user experience and customer requirements during product development.

• Design and create GenAI product frameworks, incorporating search interfaces, chatbots, and summarization tools.

• Construct and implement machine learning models and algorithms that support client growth and success.

• Provide technical leadership in spotting and assessing AI/ML opportunities.

⛳️ Requirements

• Over 5 years of practical experience in developing and deploying machine learning models in production environments.

• More than 4 years of experience with production NLP and deep learning models utilizing frameworks such as PyTorch and TensorFlow.

• At least 1 year of experience with Retrieval-Augmented Generation (RAG) and other advanced techniques aimed at optimizing model performance.

• Demonstrated experience in writing production-level code, with strong expertise in Python.

• Proficiency in working with large language models (LLMs) such as GPT, Gemini, and Claude, along with familiarity in LLM frameworks like LangChain.

• Solid understanding of prompting techniques and the trade-offs involved between prompting and fine-tuning.

• Experience with cloud platforms such as AWS or GCP (AWS preferred), or equivalent on-premise solutions.

🏝️ Benefits

• Ownership through equity participation.

• Annual company retreat.

• Education bonus for ongoing learning.

• Company-wide winter break.

• Paid time off.

• Optional in-person events and meetups.

• Customized career roadmaps.

• High-performance culture.