AI Enablement Engineer

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Payliance

WebsiteLinkedInAlle Stellen

51 - 200 Mitarbeiter

💳 Fintech

🤝 B2B

💰 €31.000.000 Debt Financing - Payliance im 2019-12

Fintech • B2B

Payliance ist ein Zahlungs-Technologieunternehmen, das eine Payments-as-a-Service (PaaS) Plattform anbietet, welche Zahlungslösungen (ACH, Kredit-/Debitkarten, Echtzeitzahlungen, Scheck-basierte), Verifikations- und Risikobewertungstools sowie Schuldeneintreibung und Forderungsmanagement umfasst. Sie bedienen Händler, Kreditgeber, Inkassobüros und BNPL-Anbieter, bieten Integrationen, APIs, Partnerprogramme (ISOs, Kreditmanagementsysteme) und Compliance-/lizenzierte Inkassodienstleistungen an. Payliance legt den Schwerpunkt auf die Reduzierung von Verarbeitungskosten, Betrugsrisiken und die Verbesserung der Rückgewinnungsquoten; sie bearbeiten große Volumina (berichtete $63B+ jährlich, 162M Transaktionen, 40K Händlerstandorte) und unterstützen Kreditvertikalen (Prime, Subprime, Earned Wage Access, BNPL) sowie Händler.

Beschreibung

• Operate and evolve Payliance's AI inference platform on Amazon Bedrock, including model selection, routing logic, and version pinning across the Claude model family. • Build and maintain internal AI services and integration layers (C#/.NET, Python) that connect Claude to enterprise systems and workflows. • Design cost-aware inference strategies — prompt caching, model tiering, and intent-based routing — that balance capability against spend. • Own the reliability of AI services with the same rigor applied to the payment platform: define SLOs, build observability (logging, tracing, alerting), plan capacity, and design for graceful degradation when models or upstream services falter. • Establish disciplined change management for AI infrastructure — pinned model versions, staged rollouts, and regression testing — so behavior never drifts silently in production. • Design, build, and maintain Claude agents, Skills, and MCP (Model Context Protocol) integrations that connect AI to internal data sources and tools. • Develop reusable agent patterns — retrieval, tool use, structured output, multi-step workflows — that other teams can adopt without starting from scratch. • Author and curate high-quality prompts, skill definitions, and agent instructions, with versioning and review discipline. • Evaluate agent quality systematically: define eval criteria, test for regressions, and validate behavior before promotion to production. • Operate and extend the internal AI skill/agent marketplace: submission pipelines, staging and curation workflows, and publication gates. • Enable non-technical employees — business analysts and beyond — to create and submit agents and Skills through low-friction workflows that don't require engineering tooling or source-control accounts. • Serve as a formal review gate for submitted agents and Skills: assess security posture, data access, prompt quality, and fitness for purpose before publication. • Manage distribution across surfaces — Claude Enterprise, Claude Code, and internal applications — with consistent configuration and rollout controls. • Design and enforce identity-aware access to AI capabilities: SSO/SCIM provisioning, group-based entitlements, and per-user permission propagation to downstream data sources. • Ensure AI tools respect existing data permissions — users should never see data through an agent that they couldn't access directly. • Author and review least-privilege IAM policies for AI infrastructure; avoid broad credential grants in favor of scoped, auditable access patterns. • Establish and maintain AI governance controls appropriate to a PCI-regulated payments environment: data handling policies, audit trails, and model usage boundaries. • Build and maintain AI usage analytics and reporting: adoption metrics, token consumption, and cost breakdowns by team and use case. • Deliver operational visibility to executive stakeholders through automated reporting and dashboards. • Monitor for misuse, anomalous usage patterns, and quality degradation across deployed agents and Skills. • Continuously optimize the cost/performance profile of AI workloads as models, pricing, and usage patterns evolve. • Partner with business teams to identify high-value AI use cases and translate them into working agents, Skills, and workflows. • Train and coach non-technical builders on effective prompt design, agent construction, and responsible AI use. • Author reference architectures, design decisions, and integration patterns that other engineering teams adopt — contributing to Architectural Services' broader practice. • Collaborate with platform engineering and security teams on architecture decisions, integration patterns, and compliance requirements. • Champion pragmatic AI adoption: cut through hype, set realistic expectations, and demonstrate measurable value.

🎯 Anforderungen

• 5+ years in software engineering, platform engineering, or DevOps, with 1+ years of hands-on experience building with large language models in production. • Practical LLM platform depth: Amazon Bedrock (or equivalent), model APIs, prompt engineering, structured outputs, and agentic/tool-use patterns. • Software engineering ability in C#/.NET or Python — can design, build, and debug production services, not just scripts. • Reliability engineering discipline: experience defining SLOs, building observability, designing for failure and graceful degradation, and operating services that other teams depend on. • AWS fluency: compute (Lambda, ECS Fargate), IAM, networking fundamentals, and infrastructure-as-code (CloudFormation or CDK). • Identity and access architecture experience: SSO (Entra ID or similar), SCIM provisioning, OAuth flows, and least-privilege permission design. • Security-first mindset with practical experience scoping data access and building auditable, governed systems. • CI/CD and workflow automation experience (GitHub Actions or similar) for building submission, review, and publication pipelines. • Exceptional communication skills — able to teach AI concepts to non-technical audiences and translate business needs into technical designs.

🏖️ Vorteile

• Competitive Base Salary based on experience. • Performance-based annual bonus. • Medical, Dental, and Vision insurance. • 401(k) with company match. • Generous PTO plus paid company holidays. • Company-paid life and long-term disability insurance. • Paid parental leave.