Junior ML Engineer

IBM Automation and AI group is seeking an AI/ML Engineer specializing in Large Language Models (LLMs) to join their AI/ML Center of Excellence team. The role involves collaboration with cross-functional product teams to implement AI capabilities across IBM's Automation product portfolio.

• Experience with NLP and large language models, including transformer architectures, model evaluation, and algorithm design
• Strong programming skills in Python and familiarity with ML frameworks such as PyTorch, TensorFlow, or JAX
• Experience with data processing pipelines and large datasets
• Knowledge of MLOps practices and tools for model deployment and monitoring
• Ability to work independently and collaborate across diverse teams
• Experience with fine-tuning and prompt engineering for LLMs
• Deep understanding of transformer architectures and attention mechanisms
• Proficiency in vector databases and embedding technologies
• Knowledge of model serving frameworks like TensorRT, ONNX, and TorchServe
• Familiarity with cloud platforms including IBM Cloud, AWS, Azure, and GCP

Data Collection and Management for LLM Evaluation and Training:

• Design and implement data collection pipelines for diverse LLM training datasets using IBM AI Model & Data Catalog
• Develop data quality assessment frameworks
• Create annotation guidelines and workflows for domain-specific datasets
• Implement data governance protocols ensuring compliance with privacy and ethical AI principles
• Establish evaluation datasets and benchmarks leveraging FM-Eval and Unitxt

LLM Integration and Implementation:

• Architect solutions to integrate LLMs with IBM's products and ecosystem
• Develop APIs and interfaces for LLM interaction with software components
• Optimize LLM deployment across cloud, edge, and on-premises environments
• Implement model compression, quantization, and optimization techniques
• Design and implement prompt engineering frameworks

AI/ML Best Practices and Innovation:

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