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About This Role
About Aperia Solutions
Aperia Solutions is a leading payments SaaS company headquartered in Dallas, Texas. We build risk management, residuals accounting, compliance, and analytics platforms used by merchant banks, ISOs, payment processors, and other participants in the credit card payments ecosystem. Operating under PCI DSS 4.0 compliance across dual on-premises datacenters (Allen, TX and Chicago, IL) plus Azure cloud, we process high-volume payment data for some of the industry's largest players.
We're in the middle of a significant platform modernization program: a next-generation risk analysis and scoring platform on Microsoft Fabric, an AI-powered ingestion platform, new AI-native applications, and ongoing evolution of the products our clients rely on every day. The teams executing this work are deliberately small and senior, with executive-level support and minimal bureaucracy. Every senior hire is a founding contributor to the architecture and the code.
About the Role
Aperia is hiring a mid-senior level software engineer to join the team building our next-generation risk analysis and scoring platform, with opportunities to contribute across our wider product portfolio — the AI-powered ingestion platform, residuals and compliance products, and client-facing web applications. You'll work under the technical direction of our Lead Software Engineer, alongside a Senior Data Platform Engineer and a Senior DevOps engineer, with big-picture system design owned by dedicated architects. It's a deliberately small, senior-leaning team — which means real features, real code review, and real growth, not ticket-grinding.
Day to day, you'll build production C#/.NET services and REST APIs, develop React frontends for client-facing applications, and contribute to our Python-based agentic work as you grow into it. This is a role for an engineer with a few strong years behind them who wants to level up fast: you'll get direct mentorship from senior engineers, exposure to a modern Azure/Kubernetes platform, and a team culture that treats AI-assisted development as a first-class skill.
What You'll Do
- Build and ship production C#/.NET services and REST APIs serving high-volume payment data under PCI DSS constraints.
- Develop client-facing web application features in React and TypeScript, from component work through API integration.
- Own well-scoped features end to end on the risk platform build — design discussion, implementation, code review, testing, and deployment.
- Contribute to our LangGraph-based agentic application — tool integrations, tests, and evals — growing your Python and LLM-engineering skills along the way.
- Write automated tests (unit, integration, end-to-end) as part of everyday development, and participate actively in code review — giving feedback, not just receiving it.
- Ship through Azure DevOps CI/CD pipelines to AKS, with support from the team's DevOps engineer.
- Use AI-assisted development tools (Claude Code, Cursor, GitHub Copilot) as a core part of your workflow, following documented team standards.
- Pair with senior engineers and participate in design reviews, growing toward ownership of larger workstreams across the portfolio.
What You Bring
- 3–5 years of professional software engineering experience shipping production systems.
- Solid C#/.NET skills — ASP.NET Core, REST API design, and comfort working in an established service architecture.
- Working proficiency in TypeScript and React, with production frontend experience.
- Relational database fundamentals — schema design, sensible queries, and an understanding of transactions.
- A habit of testing your own work — you write meaningful tests without being asked and treat code review as part of the craft.
- Clear written and verbal communication — you can explain a technical decision, ask good questions, and take feedback well.
- Comfort working in a cloud environment — you can follow a deployment through a CI/CD pipeline and debug what's yours, even if you don't own the infrastructure.
- Genuine enthusiasm for AI-assisted and AI-native development. You don't need LLM production experience, but you should be curious, current, and eager to build with these tools.
Nice to Have
- Exposure to building with LLMs — LangGraph/LangChain, RAG, prompt engineering, or even serious personal projects. We'd rather see curiosity and momentum here than years of experience.
- Python proficiency beyond scripting.
- Hands-on Azure experience — AKS/Kubernetes, App Services, or Azure DevOps administration.
- Payments or financial services background; any exposure to PCI-scoped systems.
- Familiarity with messaging patterns (Azure Service Bus, Event Hubs, Kafka) or observability tooling (OpenTelemetry, Application Insights).
- OAuth2/OIDC or other identity standards experience.
Why Aperia
- Portfolio-wide technical influence — the code and engineering standards you set shape multiple products serving a multi-hundred-million-dollar industry, and they ship to production.
- Direct working relationship with the SVP of Research & Development; small senior teams; minimal bureaucracy.
- Genuine engineering culture — technical correctness valued over slideware; extensive design documentation; real design reviews; ADR discipline.
- Explicit team commitment to AI-assisted development (Claude Code, Cursor, GitHub Copilot) — treated as a first-class productivity multiplier, not a novelty. Team standards documented and enforced.
- Modern tech stack: C#/.NET, React, TypeScript, Python, LangGraph, Azure, AKS, Kubernetes, Azure DevOps, Airflow, Microsoft Fabric, Kogito/DMN, OpenBao.
Eligibility Requirements
- Must be willing to submit to a background investigation and drug test as part of the selection process.
Job Type
- Full-time
Schedule
- Hybrid / Monday to Friday
Work Location
- Dallas, TX
Benefits
- Health insurance
- Health savings account
- Dental insurance
- Vision insurance
- 401(k) matching
- Life insurance
- Paid time off
- Parental leave
- Disability insurance
- Childcare assistance
- Education reimbursement
- Fitness membership
- Volunteer time off
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Aperia, 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Aperia AI Hiring
Aperia has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Dallas, TX, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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