This position is in-office, located in Waukee, Iowa.The GigThe 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 - VizyPay's proprietary CRM platform - and across the organization's 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.
Requirements
Ready to Level Up?- Bachelor's 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).
