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
- Bachelor's or master's degree or higher in Computer Science Machine Learning AI or related technical field
- Less than one year of experience in machine learning engineering or data science roles
- Demonstrated knowledge of 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 effectively across diverse teams.
- Strong communication skills to explain complex AI concepts to diverse audiences
- Problem-solving mindset with ability to navigate technical and business constraints
- 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)
- Commitment to continuous learning in the rapidly evolving field of AI