Interested in this AI Software Engineer role at Veryon?
Apply Now →Skills & Technologies
About This Role
Description:
About Veryon
Veryon is a leading software and technology company that exists to enable aviation teams around the world to improve efficiency and safety. Our products maximize uptime for aircraft maintenance teams through customer\-driven innovation and world\-class customer service.
With more than 7,500 customers across 137 countries, Veryon serves the general aviation, business aviation, military/defense, commercial aviation, and OEM industries.
At Veryon, we are an AI\-forward company focused on driving innovation and efficiency through emerging technologies. We prioritize hiring individuals who embrace AI, think creatively about its application, and continuously evolve alongside rapidly advancing technologies.
About the Role
The Software Engineer – AI \& Machine Learning is responsible for designing, developing, and maintaining modern software applications that leverage machine learning, natural language processing (NLP), clustering algorithms, and Generative AI to automate defect analysis and improve operational intelligence.
In this role, you will build scalable software solutions that reduce manual intervention in diagnostics, enhance operational efficiency, and deliver intelligent automation across aviation maintenance workflows. You will collaborate closely with Engineering, Product, QA, and Operations teams to develop AI\-powered capabilities that directly impact customer outcomes.
Job Duties
- Design, develop, and maintain scalable software applications using Python and modern web frameworks such as Django, Flask, or FastAPI.
- Develop and enhance machine learning models, clustering algorithms, and NLP solutions for automated defect analysis and pattern recognition.
- Build AI\-powered diagnostic systems that intelligently classify, categorize, and route operational tickets.
- Design, optimize, and maintain SQL databases, queries, and data models that support large\-scale analytics and real\-time processing.
- Develop and maintain RESTful APIs supporting AI\-powered applications and enterprise integrations.
- Improve clustering logic to reduce manual intervention in defect categorization and operational workflows.
- Collaborate with Product Management, QA, Engineering, and Operations teams to translate business requirements into scalable technical solutions.
- Research, evaluate, and implement emerging AI, machine learning, and software engineering technologies that improve product capabilities.
- Participate in Agile ceremonies including sprint planning, code reviews, retrospectives, and technical design discussions.
- Continuously improve application performance, scalability, maintainability, and software quality.
- Document technical designs, AI models, engineering standards, and development best practices.
Requirements:
- 4–8\+ years of experience in software development using Python and modern web frameworks.
- Strong proficiency with SQL, database design, query optimization, indexing, and performance tuning.
- Hands\-on experience with machine learning frameworks including Scikit\-learn, TensorFlow, PyTorch, Pandas, and NumPy.
- Strong experience developing clustering algorithms including K\-Means, DBSCAN, and Hierarchical Clustering.
- Experience building Natural Language Processing (NLP) solutions including text classification, entity extraction, sentiment analysis, and language models.
- Experience with Generative AI concepts and AI\-assisted software development workflows.
- Strong understanding of feature engineering, data preprocessing, model validation, and model optimization.
- Experience designing, developing, and consuming REST APIs.
- Knowledge of data pipeline architecture, ETL processes, and real\-time data processing.
- Experience working with relational and NoSQL databases supporting analytics workloads.
- Familiarity with Git, CI/CD pipelines, automated testing, and modern DevOps practices.
- Bachelor's degree in Computer Science, Computer Engineering, Data Science, Machine Learning, Artificial Intelligence, or a related technical field.
Preferred Skills
- Experience developing AI solutions for predictive maintenance, defect analysis, operational intelligence, or industrial automation.
- Experience with LLMs, prompt engineering, Retrieval\-Augmented Generation (RAG), AI agents, vector databases, embeddings, or semantic search.
- Experience working with cloud AI platforms such as Azure AI, AWS AI/ML, or Google Vertex AI.
- Experience building scalable ML pipelines using MLOps principles.
- Familiarity with containerization technologies such as Docker and orchestration platforms such as Kubernetes.
- Experience working in Agile/Scrum software development environments.
- Strong analytical thinking, troubleshooting, and problem\-solving skills.
- Excellent communication skills with the ability to explain complex technical concepts to technical and non\-technical stakeholders.
- Passion for continuous learning and adopting emerging AI technologies to solve real\-world business challenges.
Our Core Values
- Fueled by Customers: Customers are at the core of every decision.
- Win Together: Collaboration is our competitive edge.
- Make It Happen: No excuses. Just outcomes.
- Innovate to Elevate: We boldly challenge what’s standard and lift what’s possible.
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 4,317 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Veryon, 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 $218,500 based on 729 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.
Veryon AI Hiring
Veryon has 1 open AI role right now. They're hiring across AI Software Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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 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).
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 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.