AI ML Engineering internship: SVL

IntroductionIBM Automation and AI group is seeking an AI/ML Engineer intern specializing in machine learning and Large Language Models (LLMs) to join our AI/ML Center of Excellence team at the IBM Silicon Valley Lab location. In this role, you will collaborate with cross-functional product teams to implement cutting-edge AI/ML capabilities across IBM's Automation product portfolio. You will lead efforts in data collection for LLM training and evaluation, integrate LLM technologies into existing and new products, and establish best practices for AI/ML feature implementation by product teams.Your Role And ResponsibilitiesData Collection and Management for LLM Evaluation and Training
Design and implement robust data collection pipelines for diverse LLM training datasets leveraging the IBM AI Model & Data Catalog.
Develop data quality assessment frameworks to ensure training data meets IBM's high standards
Create annotation guidelines and workflows for specialized domain-specific datasets
Implement data governance protocols that ensure compliance with privacy regulations and ethical AI principles following the IBM Data & Model Governance process and tooling.
Establish evaluation datasets and benchmarks to measure LLM performance across various use cases leveraging FM-Eval and Unitxt.

LLM Integration and Implementation
Architect solutions to integrate LLMs with IBM's existing and emerging products and ecosystem
Develop APIs and interfaces that enable seamless interaction between LLMs and other software components
Optimize LLM deployment for various computing environments (cloud, edge, on-premises)
Implement techniques for model compression, quantization, and optimization to improve inference efficiency and minimize resource requirements
Design and implement prompt engineering frameworks for consistent LLM behavior across products

AI/ML Best Practices and Innovation
Establish technical standards and best practices for AI/ML feature implementation
Create reusable components and design patterns for common LLM use cases
Develop monitoring systems to track model performance, drift, and potential biases
Research and implement techniques for responsible AI, including explainability and fairness
Collaborate with product teams to identify opportunities for AI-driven innovation