Paysafe

GenAI Product Engineer

Paysafe

full-time

Posted on:

Location Type: Hybrid

Location: Sofia • Bulgaria

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About the role

  • Ship to production, measure usage, and iterate based on real feedback and operational data
  • Partner with domain experts to replace manual decision workflows with reliable, auditable AI-assisted systems
  • Design and operate GenAI solutions including retrieval, evaluation, monitoring, and cost-aware inference pipelines
  • Build end-to-end AI products across UI, services, and platform layers (stack chosen pragmatically)
  • Create reusable AI capabilities and internal platforms other teams can safely build on
  • Design human-AI interactions where outputs are understandable, verifiable, and actionable
  • Release incrementally to real users and continuously improve based on adoption and performance metrics

Requirements

  • Have built and operated real production systems and want to apply that rigor to GenAI products
  • Be familiar with modern LLM ecosystems and eager to evaluate and evolve approaches rather than follow a fixed framework
  • Think in systems, feedback loops, and operational behavior — not just features
  • Be comfortable working in 0→1 environments while bringing structure and reliability
  • Care about usability, developer experience, and operator trust in AI outputs
  • Bonus: experience with evaluation, observability, or operating ML/AI systems in production
Benefits
  • 25 days annual paid leave
  • 4 days paid volunteering time a year through our Paysafe Giving initiative
  • Health insurance
  • Sports card
  • Team events
  • Company discounts
  • Variety of soft skills, business and technical training programs
Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools
GenAI solutionsAI productsAI capabilitiesproduction systemsLLM ecosystemsevaluationobservabilityML systemsAI-assisted systemsinference pipelines
Soft Skills
collaborationsystem thinkingadaptabilityusability focusdeveloper experiencetrust in AI outputsiterative improvementoperational behaviorreliabilityfeedback loops