Education and Experience

• Master’s or Ph.D. in Computer Science, Artificial Intelligence, Data Science, or a related technical field, or 4 years relevant work experience. 
• Several years of experience in AI engineering, including a track record of successful project completion and innovation. 
• Publication of research in AI or contributions to open-source AI projects would be a plus. 

Supervisory Responsibilities

• Mentoring junior team members, providing guidance on AI/ML best practices. 
• Leading project teams and initiatives, ensuring timely delivery of objectives and ensuring adherence to best practices. 
• Educating non-technical team members on AI/ML concepts and initiatives as needed 

Responsibilities

• 
  
Data Preprocessing and Analysis: 
• Cleaning and preprocessing large datasets of spectral data to ensure quality and consistency. 
• Analyzing spectral data to identify patterns, anomalies, and significant features. 
• Handling noise reduction and signal processing to improve data quality. 
Model Development and Implementation: 
• Designing and developing machine learning models to analyze and interpret spectral data. 
• Implementing advanced algorithms like neural networks, support vector machines, or random forests tailored for spectral data analysis. 
• Optimizing and fine-tuning models for accuracy and efficiency. 
• Experience in Large Language Models (LLM) and Retrieval-Augmented Generation (RAG) for Generative AI 
• Leveraging LLMs and RAG to address complex business challenges
Feature Engineering and Selection: 
• Identifying and extracting relevant features from spectral data that contribute to meaningful insights. 
• Applying techniques such as principal component analysis (PCA) to reduce dimensionality and improve model performance. 
Cross-Disciplinary Collaboration: 
• Collaborating with domain experts (like chemists or physicists) to understand the context and application of spectral data. 
• Collaborate with data collection team to ensure sufficient data breadth and depth to meet project goals 
• Collaborate with Product, ATI, and CIO teams to ensure implementation of ML/AI pipelines in production 
• Communicating with other data scientists, engineers, and stakeholders to align AI/ML objectives with broader project goals 
Model Validation and Testing: 
• Rigorously testing and validating models against known datasets to ensure reliability and accuracy. 
• Employing cross-validation techniques to assess model performance and generalize ability. 
Research and Development: 
• Staying abreast of the latest developments in AI/ML as well as spectral analysis techniques. 
• Researching and experimenting with new methods and technologies to enhance modeling capabilities. 
Documentation and Reporting: 
• Documenting the development process, model architectures, and performance metrics. 
• Preparing reports and presentations for both technical and non-technical audiences to communicate findings and insights.  

Physical Requirements

• Ability to remain in a stationary position for prolonged periods, typically sitting at a desk, to perform coding and data analysis. 
• Manual dexterity to operate computers and/or other necessary technology. 

Travel Requirements

• Able to travel 1-4 weeks per year, with travel and food expenses paid by the company.