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About This Role
Company Overview:
One Park Financial (OPF) is a leading Financial Technology company dedicated to empowering small businesses by connecting them with flexible financing and funding options. Our mission is to provide entrepreneurs with the working capital they need to elevate their businesses. At OPF, we believe in working with high-performing individuals who are ready to play an integral part in our company's expansion — because our success hinges on our people.
Why Join Us? Position Overview
At OPF, we foster a dynamic and inclusive company culture that emphasizes collaboration, innovation, and personal growth. Our team is composed of passionate, driven individuals who are committed to making a difference. Here’s what you can expect when you join our team:
- Innovative Environment: Work with cutting-edge technology and be part of a team that is constantly pushing the boundaries of fintech.
- Professional Growth: We invest in our employees’ growth with continuous learning opportunities, training programs, and career advancement paths.
- Supportive Culture: Enjoy a supportive and inclusive work environment where your ideas are valued, and your contributions make a real impact.
- Community Focus: Be part of a company that understands the importance of small and mid-sized businesses to their communities and the nation’s financial health.
- High-Performing Team: Join a team of badasses who are committed to excellence and are integral to our company's expansion and success.
Position Overview
We are looking for an AI-Native Application Software Engineer to help design, build, evolve, and operate a modern cloud-based software platform serving thousands of small and micro-businesses.
This is not a traditional software engineering role where success is primarily measured by how much code an engineer writes. The modern engineer understands the business problem first, frames it precisely, uses AI to accelerate design and implementation, understands how the resulting solution fits into the broader technology ecosystem, and owns whether the solution works for the customer.
AI is an integral part of the engineering process. The engineer uses AI to research, design, generate code, create tests, investigate problems, explore alternatives, document systems, and accelerate delivery—while directing, challenging, and validating AI-generated work.
What we are looking for
- Deep Subject-Matter Expertise Understands the business and knows what “correct” means.
- Exceptional Problem Framing Turns ambiguity into clear requirements and solutions.
- AI Orchestration Uses AI effectively while challenging and validating its output.
- Systems Thinking & Breadth Understands how UI, applications, APIs, data, cloud, security, and integrations fit together.
- Curiosity & Continuous Learning Naturally investigates, experiments, learns, and adapts
What you'll do
- Understand customer workflows and translate business needs into software solutions.
- Turn ambiguous problems into clear requirements, architecture, and acceptance criteria.
- Use AI as an engineering collaborator for research, design, coding, testing, debugging, documentation, and optimization.
- Review and challenge AI-generated solutions and remain accountable for the final result.
- Develop full-stack applications using React, Node.js, JavaScript/TypeScript, and MongoDB.
- Build and operate cloud applications using AWS, Docker, Kubernetes/EKS, CloudFront, ALB, Route 53, ACM, and related services.
- Work with multi-tenant websites, customer-owned domains, DNS, SSL certificates, and scalable cloud infrastructure.
- Build business workflows including websites, lead capture, estimates, invoices, receipts, business email, analytics, and communications.
- Integrate external services such as Twilio, WhatsApp Business Platform, Amazon Connect, Google Analytics, and other APIs.
- Diagnose and resolve production issues across the entire technology stack.
- Automate repetitive engineering processes and continuously improve development and deployment workflows.
- Learn and experiment with new AI models, tools, APIs, and architectures.
Qualifications
- Strong experience with modern full-stack application development.
- Strong React, Node.js, JavaScript/TypeScript, and database experience.
- Experience with cloud-based applications, preferably AWS.
- Experience working with APIs, integrations, CI/CD, and production systems.
- Strong analytical and troubleshooting skills.
- Ability to work independently and across technical boundaries.
- Demonstrated ability to use AI effectively in software development.
- Most importantly, the ability to understand the problem, ask the right questions, and own the outcome.
Requirements
- 3+ years of professional software development experience or equivalent demonstrated capability.
- Strong JavaScript/TypeScript experience.
- Strong React experience.
- Strong Node.js experience.
- Experience developing REST APIs.
- Experience with MongoDB or another modern database.
- Experience with Git and modern software development workflows.
- Experience developing and debugging production applications.
- Understanding of cloud-based application architecture.
- Strong problem-solving ability.
- Ability to work across the full technology stack.
Preferred Experience
- AWS cloud development and operations.
- Docker and Kubernetes/EKS.
- MongoDB Atlas.
- TypeScript.
- Playwright or comparable end-to-end testing.
- Google Analytics and Looker Studio.
- Twilio, WhatsApp Business Platform, or Amazon Connect.
- AWS End User Messaging and SES.
- DNS automation and customer-owned domain management.
- Multi-tenant SaaS platforms.
- Mobile WebViews.
- Cursor or similar AI development environments.
- AI coding agents and agentic development workflows.
Benefits
- Competitive salary
- Health insurance
- Growth and development opportunities
- Dynamic and collaborative work environment
- Company events and team-building activities
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 9,079 AI roles we're tracking, AI Software Engineer positions make up 5% of the market. At One Park Financial, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $220,000 based on 1,601 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
One Park Financial AI Hiring
One Park Financial has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Miami, FL, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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