AI Engineer

VizyPay is seeking an AI Engineer to lead the design, deployment, and governance of enterprise AI solutions within a security-first payments environment. This onsite role in Waukee, IA focuses on building a scalable AI platform across VEXIS and related systems, with emphasis on safety, measurable impact, auditability, resilience, and cost efficiency. The position reports to executive leadership and collaborates across business units to turn complex challenges into production-grade AI capabilities.

Responsibilities

• Define and drive the enterprise AI strategy and multi-year roadmap with the CIO and senior leaders; collaborate with business units to identify, prioritize, and validate AI use cases.
• Establish KPIs for each AI initiative; track ROI, adoption, and operational impact; forecast and manage AI platform and inference spend within approved budgets.
• Lead build-vs-buy evaluations of AI platforms and models (commercial APIs, open-weight models, managed cloud services) against cost, security, latency, scalability, and compliance, supported by total cost of ownership analysis.
• Coordinate with Product and other groups to align AI initiatives with platform architecture, security controls, and product roadmaps.
• Set AI engineering standards and reusable patterns; mentor engineers and lead architecture reviews; partner with Learning & Development to create AI enablement guidelines and training.
• Monitor evolving AI regulations and industry guidance (EU AI Act, US state AI statutes, card-network requirements) and update governance accordingly.
• Architect and operate a secure, scalable enterprise AI platform including LLM gateway and model routing (Anthropic/OpenAI APIs, AWS Bedrock, Azure OpenAI), prompt/version management, vector search and RAG pipelines, evaluation harnesses, and cost/usage guardrails.
• Deliver production AI solutions in VEXIS and adjacent systems—agent/merchant experiences, intelligent document processing, workflow automation, analytics copilots, and productivity tooling—selecting methods ranging from classical ML to LLM- and agent-based approaches.
• Build agentic AI workflows with human-in-the-loop controls, least-privilege tool access, and rollback safety; integrate AI with enterprise systems via secure APIs, webhooks, event-driven patterns, and internal MCP services.
• Implement rigorous LLM/MLOps practices: observability, structured evaluation, A/B testing, regression testing, drift monitoring, and inference cost/latency optimization (caching, routing, tiering, token budgeting).
• Ensure resilience of AI-dependent workflows (RTO/RPO alignment, provider failover, model fallback, graceful degradation); manage releases under formal change control and assume production ownership for AI services.
• Establish enterprise AI governance: acceptable-use policy, model risk classification, data-handling standards, human-oversight requirements, and due diligence for AI vendors and services.
• Engineer AI systems secure-by-design aligned with PCI DSS and financial obligations: least privilege access, data classification/minimization, defined retention, and strict exclusion of cardholder data from prompts, training data, embeddings, and logs.
• Apply OWASP Top 10 for LLM applications across design and review; collaborate with InfraSec on threat modeling and runtime guardrails (input/output filtering, policy enforcement, abuse detection).
• Maintain audit-ready documentation for each production AI system—model/system cards, architecture decision records, and data lineage—and define responsible-AI standards for fairness, transparency, and disclosure of AI-assisted decisions.

Requirements

• Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent experience.
• 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.
• AI/ML engineering certifications such as AWS Certified Machine Learning – Specialty, Microsoft Azure AI Engineer Associate (AI-102), or Databricks Generative AI Engineer Associate are preferred.
• AI governance and security certifications such as IAPP AI Governance Professional, ISO/IEC 42001 Lead Implementer, or ISACA Advanced in AI Audit are preferred.
• Experience building an AI function, platform, or practice from the ground up in an organization without prior AI infrastructure.
• Experience in security- or compliance-constrained environments (PCI DSS, SOC 2, or financial services regulation) with formal SDLC and change management.
• Strong SQL and production relational databases (PostgreSQL, SQL Server, MySQL) with in-database vector search; ETL/ELT pipelines, data modeling, and data quality for AI readiness.
• Proficiency in Python and/or TypeScript, API design, event-driven integration (REST, webhooks, queues/streams), cloud services (AWS, Azure), containers, serverless/edge compute, and infrastructure-as-code (Terraform).
• Solid understanding of classical machine learning methods and disciplined model validation.
• Experience with RAG architectures, embeddings, vector databases (pgvector, Pinecone, Weaviate, Qdrant, OpenSearch), prompt engineering and versioning, structured outputs, function/tool calling, and multi-step agent orchestration.
• Familiarity with LLM observability/evaluation platforms (Langfuse, LangSmith, Arize Phoenix), model lifecycle tools (MLflow, Weights & Biases), and CI/CD for AI (GitHub Actions).
• Experience with OCR and structured extraction (Azure Document Intelligence, AWS Textract, Google Document AI) or LLM-based extraction pipelines; OAuth 2.0/OIDC and service-to-service authentication, secrets management, and RBAC for AI tools and data access.
• Proven ability to translate ambiguous business problems into shipped AI capabilities with measurable outcomes and to communicate strategy and tradeoffs to executives.
• Demonstrated technical leadership: mentoring, architecture reviews, standards ownership, or team leadership.

Technologies

• Anthropic/OpenAI APIs, AWS Bedrock, Azure OpenAI
• VEXIS
• MCP (Model Context Protocol)
• GitHub Actions
• Langfuse, LangSmith, Arize Phoenix
• MLflow, Weights & Biases
• pgvector, Pinecone, Weaviate, Qdrant, OpenSearch
• Azure Document Intelligence, AWS Textract, Google Document AI
• OAuth 2.0/OIDC
• Terraform
• Cloudflare Workers, Lambda
• PostgreSQL, SQL Server, MySQL
• Python, TypeScript
• REST, webhooks
• AWS, Azure
• HubSpot, Microsoft 365/Graph API, QuickBooks
• LangGraph, LoRA, PEFT, vLLM, quantization
• MCP

Benefits

• 401(k)
• 401(k) matching
• Dental insurance
• Flexible spending account
• Health insurance
• Health savings account
• Paid time off
• Retirement plan
• Vision insurance