AI/ML Engineer - Hybrid Onsite

Primary Responsibilities

• Develop and implement machine learning solutions using LLMs
• Utilize the LangChain framework for working with closed source and open-source models
• Apply fine-tuning methodologies to optimize machine learning models for specific tasks and datasets
• Write clean, maintainable, and efficient code adhering to PEP8 standards
• Utilize containerization technologies such as Docker for development and deployment
• Ensure the production quality of code, including thorough testing and documentation
• Work on traditional machine learning methods like XGBoost for classification tasks and unsupervised learning techniques like K-means

Required Qualifications

• Undergraduate degree or equivalent experience
• 3+ years of proven experience working with Large Language Models (LLMs)
• 3+ years of experience with machine learning concepts and fine-tuning methodologies
• 3+ years of professional coding experience in Python, following PEP8 standards
• Experience using the LangChain framework
• Experience with Retrieval-Augmented Generation (RAG) techniques
• Experience with containerization technologies such as Docker
• Experience ensuring production-quality code, including thorough testing and documentation
• Experience with version control systems such as Git
• Proven solid problem-solving skills and attention to detail
• Proven ability to write clean, maintainable, and efficient code
• Proven comfortable working in the terminal for development and deployment tasks

Preferred Qualifications

• Healthcare experience
• Statistical knowledge

Pay

Pay is based on several factors including but not limited to local labor markets, education, work experience, certifications, etc. In addition to your salary, we offer benefits such as, a comprehensive benefits package, incentive and recognition programs, equity stock purchase and 401k contribution (all benefits are subject to eligibility requirements).

Schedule

This position follows a hybrid schedule with four in-office days per week.