D
Datasnipper
Software Engineer (Document Intelligence)
Skills
PythonC#Machine LearningAIComplianceAzure
1 month ago
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Important information
Contract type:
Permanent
Location:
Netherlands
Work mode:
Onsite
Published on:
10 September 2026
What they need
- We are looking for a Software Engineer to join the Document Intelligence Team
- Our vision is to become the document knowledge platform for DataSnipper — a shared, cloud-native foundation where every document and everything we extract from it becomes a durable, reusable asset that powers our agents and other products
- This is a hands-on engineering role in a high-caliber team that moves fast and holds a high bar
- You will build and maintain the ingestion, processing, and extraction pipelines and the platform services that expose them, contribute to architectural decisions, and deliver core parts of the document intelligence platform at DataSnipper
- You will work closely with Product, Agent teams, and fellow engineers to deliver high-quality, production-ready solutions
- Build and maintain components of the document intelligence platform that deliver best-in-class accuracy, speed, and reliability, with design direction from senior engineers
- Build and maintain the platform APIs and services that expose documents and their extracted intelligence as shared, reusable capabilities for multiple products and workflows
- Help build and operate the cloud document store: a secure, managed store for every document we ingest plus everything we extract, with the metadata, retrieval patterns, and traceability that downstream intelligence requires
- Integrate with AI service providers to power document understanding at scale
- Build AI- and ML-powered extraction capabilities
- Work within the integration contracts between platform APIs and consuming teams, and flag gaps early
- Apply well-established design principles within an existing architecture, accounting for document-heavy use cases (large PDFs, mixed formats, high-volume batches)
- Participate in code reviews and follow team best practices
- Build and use quality metrics; repeatable pipelines that score extraction quality on representative production documents to identify gaps and drive measurable improvements
- Implement the security and compliance requirements defined for document handling (retention, deletion, access controls, auditability, and regional requirements)
- Break down your own work into deliverable increments and surface cross-team dependencies early
- Work with product partners to clarify requirements for your workstream and simplify technical approaches
- Foster a culture of documentation and knowledge sharing within the team and with business stakeholders
- Integrate AI tools into workflows, critically evaluate AI outputs, and contribute to team-level AI adoption- Experience with testing strategies (unit, integration, E2E) and a quality-first mindset
- Ownership: You own well-scoped work end-to-end, anticipate issues, and ask for guidance on ambiguous decisions
- 4–6 years in software engineering, with strong proficiency in C# and/or Python and its ecosystem and tooling
- Adaptability: You navigate ambiguity calmly, model positive behavior, and help peers adjust through clear communication
- Excellent communication and collaboration skills
- Experience working on distributed or data-intensive systems
- Growth Mindset: You encourage open feedback exchange and provide clear, balanced feedback that helps others grow
- Proficient with AI-assisted Engineering (we use Claude Code)
- Experience contributing to backend services in production on any cloud platform (we are on Azure)
- Comfortable working across the stack: either full-stack experience, or strong backend with genuine willingness to contribute to frontend work
- Judgment: You escalate early, ask good questions, and deliver reliably against agreed priorities
- Collaboration: You build strong cross-functional relationships and communicate clearly across functions
- Experience with document processing, analysis, or storage technologies and pipelines
- Experience building platform or shared-service capabilities consumed by multiple internal teams and external partners
- Experience with Azure services (AI Foundry, Container Apps, Service Bus, Blob storage, Content Understanding)
- Experience with LLM-based document understanding and evaluation
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