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Coupa is seeking a Senior AI Engineer to join its AI Platform team in Prague, working on sourcing optimization problems within its total spend management platform. The role focuses on building scalable infrastructure to support diverse AI workloads including recommendation systems, game theory applications, forecasting, combinatorial optimization, and multimodal document understanding.
You will collaborate closely with AI researchers to translate model requirements into production-ready cloud infrastructure. Key responsibilities include designing and maintaining scalable, reliable, and cost-effective AI platforms; improving data pipelines, feature storage, experiment tracking, and model lifecycle workflows; building tooling for experimentation and benchmarking; implementing monitoring and observability; and participating in architectural discussions to shape long-term platform strategy.
The team is small and senior, composed of engineers who built Rossum's production inference pipeline from scratch. You'll have direct access to Product and Research teams, ensuring that validated ideas quickly move into systems used at scale. You'll work across the full stack: Python, RabbitMQ, S3, Postgres, Triton Inference Server, deployed via Kustomize and Flux to Kubernetes on AWS.
Coupa's proprietary T-LLM (transactional LLM) architectures are designed and trained in-house to process millions of complex business documents weekly. This role offers real ownership with a short path to customers—you frame problems, choose methods, and see solutions deployed globally.
REQUIREMENTS:
Must-Haves:
- 5+ years of experience in product-minded software engineering, ML platform engineering, or infrastructure roles
- Proven track record of delivering ML solutions with measurable business and customer impact
- Strong programming experience in Python or similar ML-suitable language
- Understanding of distributed systems, microservices, and cloud-native architectures
- Experience with SQL databases (query optimization, performance tuning, schema design)
- Experience with ML tooling (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 training or serving AI/ML models at scale
- Experience 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
