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.

• Demonstrated experience with NLP and large language models (e.g., transformer architectures) including model evaluation and algorithm design
• Strong programming skills in Python and familiarity with ML frameworks (PyTorch, TensorFlow, or JAX)
• Experience with data processing pipelines and working with 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 (TensorRT, ONNX, TorchServe)
• Familiarity with cloud platforms (IBM Cloud, AWS, Azure, GCP)

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