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About This Role
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.
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\).
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At VizyPay, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
VizyPay AI Hiring
VizyPay has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Waukee, IA, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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