Sr AI Engineer - 11900

Coupa makes margins multiply through its community-generated AI and industry-leading total spend management platform for businesses large and small. Coupa AI is informed by trillions of dollars of direct and indirect spend data across a global network of 10M+ buyers and suppliers. We empower you with the ability to predict, prescribe, and automate smarter, more profitable business decisions to improve operating margins.

Why join Coupa? Pioneering Technology: At Coupa, we're at the forefront of innovation, leveraging the latest technology to empower our customers with greater efficiency and visibility in their spend. Collaborative Culture: We value collaboration and teamwork, and our culture is driven by transparency, openness, and a shared commitment to excellence. Global Impact: Join a company where your work has a global, measurable impact on our clients, the business, and each other. Learn more on Life at Coupa blog and hear from our employees about their experiences working at Coupa.

About The Team Rossum joined Coupa earlier this year. We brought the document understanding layer - our proprietary T-LLM (transactional LLM) architectures, which we design and train from scratch, and which read the world's messiest business documents in production, millions of them every week. Now we are pointing the same in-house research capability at a much bigger problem: not just reading the documents, but acting on them. About the Role Sourcing is where the money is actually decided. We are expanding our AI Platform team in Prague with a Senior AI Platform Engineer to work on a Sourcing problem set – which suppliers get invited. How the event is structured. How bids that differ in price, lead time, quality, risk and carbon get compared at all. What a fair price even is. When to award, to whom, and how to split the award across suppliers. How to do auctions and autonomous bidding. This broad problem set will not be solved by a single model. Solutions might come from a diverse set of fields Recommendation and retrieval Game theory Forecasting and should-cost modelling Combinatorial optimization Multimodal document understanding etc. You will collaborate with researchers to implement these heterogeneous workloads in a scalable production platform in the cloud. At the same time, you will design and develop infrastructure for dataset export, model training, and evaluation to allow Research to move fast. You will work in a small, senior team of engineers - the group that built Rossum's production inference pipeline from scratch - with direct access to Product and the AI Research team. Ideas that work do not sit on paper; they roll into systems used at scale.

Design, build, and maintain a scalable, reliable, and cost-effective AI platform in the cloud. Collaborate with Research to understand model requirements and translate them into scalable infrastructure. Improve data pipelines, feature storage, experiment tracking, and model lifecycle workflows. Build tooling that accelerates experimentation, benchmarking, and reproducibility. Implement monitoring, observability, and reliability improvements across AI services. Participate in architectural discussions and contribute to long-term platform strategy. Partner with Product, Research, and other engineering teams to align platform capabilities with product needs. Maintain clear documentation and support knowledge-sharing across R&D.

Must-Haves 5+ years of experience in product-minded software engineering, ML platform engineering, or infrastructure roles Proven track record of delivering ML solutions that drive measurable business and customer impact. Strong programming experience in a language suitable for ML such as Python Understanding of distributed systems, microservices, and cloud-native architectures. Experience with SQL databases (query optimization, database performance tuning, and schema design). Experience with ML tooling (e.g., experiment tracking, model registries, data pipelines). Strong problem-solving skills and ability to work in cross-functional R&D environments. Solid understanding of CI/CD, infrastructure-as-code, and observability tooling. Internal communication in English as a default. Nice-to-Haves Experience with training or serving AI/ML models at scale. Experience with building scalable, automated ETL/ELT pipelines and maintaining robust database architectures (SQL/NoSQL). Familiarity with data annotation workflows and dataset management. Experience with GPU workloads, batch/stream processing, or feature stores. Exposure to Intelligent Document Processing or Deep Neural Network architectures. Our Stack We try to keep our stack standardized and minimal: Python, RabbitMQ, S3, Postgres, Triton Inference Server. All deployed with Kustomize and Flux to a Kubernetes cluster in AWS.

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