Principal Engineer – AI​/ML Architecture

• 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.

• 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.

Expertise in architecting and optimizing machine learning systems, particularly in training and deploying large language models. Strong capability in designing evaluation frameworks and ensuring model safety and compliance within AI platforms.

• Machine Learning Systems Architecture
• Large Language Model Training
• Transformer Architectures
• Cloud AI/ML Services
• Python and PyTorch Proficiency

• Model Training
• Model Evaluation
• Distributed Training
• Training Optimization
• RAG Systems Design
• Synthetic Data Generation
• Knowledge Graph Integration
• Embedding Models
• Evaluation Suite Design
• Fine‑Tuning Models

• Mentoring
• Collaboration

• Advanced Degree in Computer Science
• Advanced Degree in Machine Learning

• B2B SaaS Platforms
• Responsible AI
• Technical Standards
• Model Safety
• Tenant Data Isolation

• GPU Clusters
• AI Infrastructure Tooling