AI Engineer

This position is in-office located in Waukee Iowa.

Vizonian Life

VizyPay is leading the technology-payment processing space. Our culture is built on trust transparency technology and talent. We are the voice for business owners putting money back in their pockets and eliminating up to 100% of their processing fees.

Its time to love what you do and be your authentic self! Yes we hold each other accountable. If youre successful were all successful - this is why we #workhardplayhard so #LFG #TeamVizy!

The Gig

The AI Engineer leads the design development deployment and governance of enterprise AI solutions. The role shapes the enterprise AI strategy builds a scalable AI platform and delivers production-grade AI capabilities embedded in VEXIS VizyPays proprietary CRM platform and across the organizations business systems. Operating in a security- and compliance-driven payments environment the role is accountable for making AI safe measurable audit-ready resilient and cost-effective in production.

AI Strategy & Leadership

• Define and drive the enterprise AI strategy and multi-year roadmap in partnership with the CIO and executive leadership; internal partnerships with business units to identify prioritize and validate AI use cases.
• Define KPIs for each AI initiative; measure and report ROI adoption and operational impact; forecast and manage AI platform and inference spend against approved budget.
• Own build-vs-buy evaluations of AI platforms and models (commercial APIs open-weight managed cloud services) against cost security latency scalability and compliance criteria supported by TCO analysis.
• Collaborate closely with other groups within the business unit and Product team to align AI initiatives with platform architecture security controls and product roadmaps.
• Establish AI engineering standards and reusable patterns; mentor other engineers and lead AI architecture reviews; work closely with L&D to develop employee AI enablement usage guidelines and training.
• Monitor emerging AI regulation and industry guidance (e.g. EU AI Act US state AI statutes card-network requirements) and adapt governance accordingly.

AI Platform & Solution Engineering

• Architect and operate a secure scalable enterprise AI platform: LLM gateway and model routing (e.g. Anthropic/OpenAI APIs AWS Bedrock Azure OpenAI) prompt and version management vector search and RAG pipelines evaluation harnesses and cost/usage guardrails.
• Deliver production AI solutions in VEXIS and adjacent systems agent/merchant experience intelligent document processing workflow automation analytics copilots and productivity tooling selecting the right technique for each problem from classical/predictive ML to LLM- and agent-based approaches.
• Build agentic AI workflows with human-in-the-loop controls action authorization least-privilege tool access and rollback safety; integrate AI with enterprise systems through secure APIs webhooks event-driven patterns and internal MCP (Model Context Protocol) services.
• Implement rigorous LLMOps/MLOps: observability and tracing structured offline/online evaluation and A/B experimentation regression testing drift monitoring and inference cost/latency optimization (caching model routing and tiering token budgeting).
• Ensure resilience of AI-dependent workflows (RTO/RPO alignment provider failover model fallback graceful degradation); operate releases under formal change management; carry production ownership including incident response for AI services.

Governance Security & Responsible AI

• Establish the enterprise AI governance framework: acceptable-use policy model risk classification data-handling standards human-oversight requirements and security/compliance due diligence for AI vendors and services.
• Engineer AI systems secure-by-design and aligned with PCI DSS and financial-industry obligations: least privilege data classification and minimization defined retention and strict exclusion of cardholder and other sensitive data from prompts training data embeddings and logs.
• Apply the OWASP Top 10 for LLM Applications across design and review; partner with InfraSec on threat modeling (prompt injection data leakage model abuse) and runtime guardrails (input/output filtering policy enforcement abuse detection).
• Maintain audit-ready documentation for every production AI system model/system cards architecture decision records and data lineage and define responsible-AI standards for fairness transparency explainability and disclosure of AI-assisted decisions.

Ready to Level Up

• Bachelors degree in Computer Science Engineering or a related field or equivalent experience required.
• 7 years of professional software engineering experience including 3 years designing building and operating production ML/AI systems at enterprise scale with accountability for reliability cost and outcomes required.
• AI/ML engineering certifications: AWS Certified Machine Learning Specialty Microsoft Azure AI Engineer Associate (AI-102) or Databricks Generative AI Engineer Associate preferred.
• AI governance and security certifications: IAPP AI Governance Professional (AIGP) ISO/IEC 42001 Lead Implementer or ISACA Advanced in AI Audit (AAIA) preferred.
• Experience establishing an AI function platform or practice from the ground up (0->1) in an organization without prior AI infrastructure.
• Experience in security- or compliance-constrained environments (e.g. PCI DSS SOC 2 or financial services regulation) delivering under formal SDLC and change management.
• Strong SQL and production relational databases (e.g. PostgreSQL SQL Server MySQL) with in-database vector search; ETL/ELT pipelines data modeling and data quality to make enterprise data AI-ready.
• Technical knowledge in Python and/or TypeScript API design event-driven integration (REST webhooks queues/streaming) cloud-native services (AWS Azure) containers serverless/edge compute (e.g. Lambda Cloudflare Workers) and infrastructure-as-code (e.g. Terraform).
• Strong understanding of classical machine learning: supervised and unsupervised techniques (classification regression clustering anomaly detection) with disciplined model validation.
• RAG architectures embeddings and vector databases (e.g. pgvector Pinecone Weaviate Qdrant OpenSearch) prompt engineering and versioning structured outputs function/tool calling and multi-step agentic orchestration.
• LLM observability and evaluation platforms (e.g. Langfuse LangSmith Arize Phoenix) model lifecycle tooling (e.g. MLflow Weights & Biases) and CI/CD for AI systems (e.g. GitHub Actions).
• OCR and structured extraction (e.g. Azure Document Intelligence AWS Textract Google Document AI or LLM-based extraction pipelines). OAuth 2.0/OIDC and service-to-service authentication vault-based secrets management and RBAC design for AI tools and data access.
• Proven ability to translate ambiguous business problems into shipped AI capabilities with measurable outcomes and to present strategy risk and tradeoffs to executive stakeholders.
• Track record of technical leadership: mentoring architecture review standards ownership or team leadership.

Take Your Career To The Next Level!

• Experience in payments fintech banking or other regulated financial industries; integrating AI with SaaS business systems (e.g. HubSpot Microsoft 365/Graph API QuickBooks).
• Building and securing MCP servers and tools; designing multi-agent systems (e.g. LangGraph or comparable orchestration frameworks).
• Fine-tuning distillation or inference optimization (e.g. LoRA/PEFT quantization vLLM); red-teaming LLM applications; AI governance aligned to recognized frameworks (NIST AI RMF ISO/IEC 42001).