Principal Engineer, AI/ML Architecture - 11310

The Impact of a Principal Engineer, AI/ML Architecture at Coupa:

Coupa's AI platform powers production agents, conversational AI (Navi), customer-built agents (Agent Studio), and a suite of ML models for classification, fraud detection, and supplier intelligence. The Principal Engineer, AI/ML Architecture will define the technical direction for the next generation of these capabilities, designing how we train, evaluate, and serve models that are deeply tuned to enterprise spend management. Reporting to the Sr. Director, you will be hands-on in architecture and prototyping while guiding a growing team of ML and data engineers.

What You'll Do:

• Define the architecture for model training, evaluation, and serving across Coupa's AI platform.
• Evaluate and select model approaches (open-weight, commercial, and hybrid) against enterprise accuracy and cost requirements.
• Design evaluation frameworks that measure model quality on Coupa's specific task categories.
• Drive technical partnership evaluations with AI infrastructure and model providers.
• Architect training data pipelines, including synthetic data generation and quality validation.
• Design retrieval-augmented generation (RAG) systems that extend our existing RAG infrastructure with structured knowledge retrieval.
• Establish technical standards for model safety, tenant data isolation, and responsible AI.
• Write code, review PRs, and prototype approaches, especially in the early phases.
• Mentor and guide ML and data engineers across US and India.
• Collaborate with product, existing AI platform, and cloud operations teams.

What You Will Bring to Coupa:

• 15+ years of software engineering experience, with 5+ years focused on ML/AI systems.
• Demonstrated experience training or fine-tuning large language models. Must have shipped a fine-tuned or domain-adapted model to production.
• Deep knowledge of transformer architectures, training optimization (LoRA, QLoRA, PEFT, RLHF, DPO), and inference serving.
• Experience with distributed training on GPU clusters.
• Strong understanding of RAG architectures, vector search, embedding models, and knowledge graph integration.
• Hands-on experience with cloud AI/ML services (model hosting, managed training, or equivalent).
• Experience designing and running custom evaluation suites for LLMs.
• Proficiency in Python, PyTorch, and ML infrastructure tooling.
• Advanced degree in Computer Science, Machine Learning, or equivalent practical experience.
• Experience with enterprise B2B SaaS platforms preferred.