Entry level AI/ML Engineer: SVL

IBM Automation and AI group is seeking an entry level AI/ML Engineer 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. Data 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

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