AI Engineer

AI Engineer in the field of spectral data modeling combines expertise in machine learning with a deep understanding of spectral analysis, playing a vital role in extracting valuable insights from complex spectral datasets. This position involves a balance of technical proficiency, collaborative skills, and continuous learning to stay at the forefront of technological advancements. The AI Engineer is responsible for leading the development and integration of artificial intelligence and machine learning algorithms into our suite of spectral data analysis tools. This role involves advanced analytics, deep learning, and the implementation of GenAI/AI-driven solutions to enhance real-time decision-making capabilities 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.

Created: 2024-11-03