Senior ML Engineer

At IBM Software, we transform client challenges into solutions. Building the world’s leading AI-powered, cloud-native products that shape the future of business and society. Our legacy of innovation creates endless opportunities for IBMers to learn, grow, and make an impact on a global scale. Working in Software means joining a team fueled by curiosity and collaboration. You’ll work with diverse technologies, partners, and industries to design, develop, and deliver solutions that power digital transformation. With a culture that values innovation, growth, and continuous learning, IBM Software places you at the heart of IBM’s product and technology landscape. Here, you’ll have the tools and opportunities to advance your career while creating software that changes the world.

• Demonstrated expertise with NLP and large language models (e.g., transformer architectures) including model evaluation and algorithm design.
• Exceptional programming skills in Python and familiarity with ML frameworks (PyTorch, TensorFlow, or JAX).
• Extensive experience with data processing pipelines and working with large datasets.
• Deep 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.