AI Engineer

Vizypay•Waukee, IA

31d•Onsite

About The Position

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. It’s time to love what you do and be your authentic self! Yes, we hold each other accountable. If you’re successful, we’re 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 — 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.

Requirements

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.

Nice To Haves

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).

Responsibilities

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.
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.
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.