AI Engineer

VizyPay is seeking an AI Engineer to lead the design, deployment, and governance of enterprise AI solutions within a security- and compliance-driven payments environment. This onsite role in Waukee, IA focuses on shaping the enterprise AI strategy, building a scalable AI platform, and delivering production-grade AI capabilities across VEXIS and connected systems, with a priority on safety, measurable impact, auditability, resilience, and cost efficiency. You will work closely with executive leadership to align AI initiatives with platform architecture, security controls, and product roadmaps.

Responsibilities

• Lead the enterprise AI strategy and multi-year roadmap in collaboration with the CIO and executive leadership; partner with business units to identify, prioritize, and validate AI use cases.
• Define KPIs for each AI initiative, measure ROI and adoption, and forecast/influence AI platform and inference spend against the approved budget.
• Lead build-vs-buy evaluations of AI platforms and models (commercial APIs, open-weight, managed cloud services) with TCO analysis, focused on cost, security, latency, scalability, and compliance.
• Coordinate with other groups to ensure AI initiatives align with platform architecture, security controls, and product roadmaps.
• Establish AI engineering standards and reusable patterns; mentor engineers; lead AI architecture reviews; collaborate with L&D on enablement and training guidelines.
• Monitor evolving AI regulations and guidance (eg, EU AI Act, US state 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 techniques from classical ML to LLM- and agent-based approaches.
• Design 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 MCP services.
• Implement rigorous LLM/MLOps practices: observability, offline/online evaluation, A/B experimentation, regression testing, drift monitoring, and inference cost/latency optimization.
• Ensure resilience of AI-dependent workflows with proper RTO/RPO alignment, provider failover, model fallback, and graceful degradation; manage releases under formal change control and own production incident response for AI services.
• Establish enterprise AI governance framework: acceptable-use policy, model risk classification, data-handling standards, human oversight requirements, and security/compliance due diligence for AI vendors.
• Engineer secure-by-design AI systems aligned with PCI DSS and financial obligations: least privilege, data classification/minimization, defined retention, and exclusion of sensitive data from prompts, training data, embeddings, and logs.
• Apply OWASP Top 10 for LLM applications and partner with InfraSec on threat modeling and runtime guardrails for prompt injection, data leakage, and model abuse.
• Maintain audit-ready documentation for production AI systems—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 responsibility 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 establishing 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) delivering under 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.
• Technical proficiency in Python and/or TypeScript, API design, event-driven integration (REST, webhooks, queues/streaming), cloud-native services (AWS, Azure), containers, serverless/edge compute, and infrastructure-as-code (Terraform).
• Solid understanding of classical machine learning techniques with disciplined model validation.
• Experience with RAG architectures, embeddings, vector databases (pgvector, Pinecone, Weaviate, Qdrant, OpenSearch), prompt engineering/versioning, structured outputs, function/tool calling, and multi-step agent orchestration.
• Experience with LLM observability and evaluation platforms (Langfuse, LangSmith, Arize Phoenix), model lifecycle tooling (MLflow, Weights & Biases), and AI CI/CD (GitHub Actions).
• OCR and structured extraction capabilities (Azure Document Intelligence, AWS Textract, Google Document AI) or LLM-based extraction pipelines; OAuth 2/OIDC, 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 present strategy, risk, and tradeoffs to executives.
• Track record of technical leadership, including 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, event-driven architectures
• AWS, Azure
• HubSpot, Microsoft 365/Graph API, QuickBooks
• LangGraph, LoRA, PEFT, vLLM, quantization

Benefits

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