LLM Researcher

Responsibilities

• Research and implement advanced Large Language Models (LLMs)

• Collaborate with Generative AI Centre of Excellence leaders and Equinix business units to assist in deciding between purchasing off-the-shelf generative AI tools and building solutions from foundational models for various generative AI applications

• Utilize complex agents with platforms like Google Agentspace, Microsoft CoPilot, and Salesforce Agentforce

• Innovate and optimize the machine learning workflow, from data exploration and model experimentation to production deployments on cloud platforms such as GCP or Azure

• Architect LLM solutions that integrate agents built on different clouds or applications into a unified platform

• Proficiently use deep learning frameworks such as PyTorch and TensorFlow

• Develop model pipelines in a development environment, manage version control with Git, utilize GitHub Actions, containerize applications, and deploy them to virtual machines, App Engine, or Kubernetes clusters

• Possess in-depth knowledge of NLP fundamentals, including transformers, attention models, and text pre-processing

• Publish NLP or Machine Learning research papers in top AI journals or conferences

• Articulate research findings into patents

• Apply cutting-edge technologies and toolchains in big data and machine learning to build a robust machine learning platform on the cloud (MLOps)

• (Good to have) Envision, implement, and deliver production-level classical machine learning models (regression, classification, clustering), NLP models (sentiment analysis, summarization, chatbot/Q&A, information retrieval), and computer vision applications (image classification, object detection, semantic segmentation, and instance segmentation using YOLO V7, DDRNet, RFTM with pre-trained datasets like COCO and Cityscapes)

• Deploy machine learning models into production using cutting-edge deployment strategies and conduct A/B tests to objectively measure performance improvements

• Continuously innovate and optimize the machine learning workflow, from data exploration and model experimentation to production deployment

• Develop features, conduct tests, perform statistical analyses, and interpret results to drive insights

Qualifications

• PhD with 4+ years of experience, Master’s with 3+ years, or Bachelor’s with 6+ years in Data Science, Computer Science, or Machine Learning

• Proficiency in Python programming is essential

• Strong understanding of software engineering principles and design patterns

• Experience with at least one major cloud platform

• Ability to effectively communicate analysis results and insights

• Excellent time management, communication, and organizational skills