{"id":1659899,"url":"https://alion.io/job/coupa-software-sr-ai-engineer-11900","title":"Sr AI Engineer","company":{"id":8573,"name":"Coupa Software","domain":"coupa.com","url":"https://alion.io/company/coupa","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"A","score":93,"open_postings":6,"ghost_share":0,"stale_share":0.167,"repost_share":0.167,"time_to_fill_p50_days":59,"computed_at":"2026-10-14T06:34:16Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Prague, Czech Republic"],"countries":["CZ"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":44000,"max_usd":112000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":1986},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CI/CD","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"Amazon S3","optional":true},{"name":"AWS","optional":true},{"name":"ETL/ELT","optional":true},{"name":"Feature Store","optional":true},{"name":"Kubernetes","optional":true},{"name":"Kustomize","optional":true},{"name":"LLM","optional":true},{"name":"PostgreSQL","optional":true},{"name":"RabbitMQ","optional":true},{"name":"Triton Inference Server","optional":true}],"status":"live","first_seen_at":"2026-09-28T15:05:18Z","employer_posted_date":"2026-09-28","last_verified_at":"2026-10-14T22:25:58Z","board_verified":true,"closed_at":null,"days_open":16,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":16},"description":"About The Team\nRossum 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.\nAbout the Role\nSourcing is where the money is actually decided.\nWe 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.\nThis broad problem set will not be solved by a single model. Solutions might come from a diverse set of fields\nRecommendation and retrieval\nGame theory\nForecasting and should-cost modelling\nCombinatorial optimization\nMultimodal document understanding etc.\nYou 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.\nYou 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.\nWhat You’ll Do\nDesign, build, and maintain a scalable, reliable, and cost-effective AI platform in the cloud.\nCollaborate with Research to understand model requirements and translate them into scalable infrastructure.\nImprove data pipelines, feature storage, experiment tracking, and model lifecycle workflows.\nBuild tooling that accelerates experimentation, benchmarking, and reproducibility.\nImplement monitoring, observability, and reliability improvements across AI services.\nParticipate in architectural discussions and contribute to long-term platform strategy.\nPartner with Product, Research, and other engineering teams to align platform capabilities with product needs.\nMaintain clear documentation and support knowledge-sharing across R&D.\nWhat You Will Bring to Coupa\nMust-Haves\n5+ years of experience in product-minded software engineering, ML platform engineering, or infrastructure roles\nProven track record of delivering ML solutions that drive measurable business and customer impact.\nStrong programming experience in a language suitable for ML such as Python\nUnderstanding of distributed systems, microservices, and cloud-native architectures.\nExperience with SQL databases (query optimization, database performance tuning, and schema design).\nExperience with ML tooling (e.g., experiment tracking, model registries, data pipelines).\nStrong problem-solving skills and ability to work in cross-functional R&D environments.\nSolid understanding of CI/CD, infrastructure-as-code, and observability tooling.\nInternal communication in English as a default.\nNice-to-Haves\nExperience with training or serving AI/ML models at scale.\nExperience with building scalable, automated ETL/ELT pipelines and maintaining robust database architectures (SQL/NoSQL).\nFamiliarity with data annotation workflows and dataset management.\nExperience with GPU workloads, batch/stream processing, or feature stores.\nExposure to Intelligent Document Processing or Deep Neural Network architectures.\nOur Stack\nWe try to keep our stack standardized and minimal: Python, RabbitMQ, S3, Postgres, Triton Inference Server. 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