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About This Role
Company Overview:
Lightspeed is the leading provider of cloud\-based software for dealerships, serving the Powersport, Marine, RV, Trailer, and Golf Car industries adding hundreds of dealerships to the Lightspeed community each year.
Lightspeed's Dealer Management Solution (DMS) enables dealerships to optimize their end\-to\-end business operations, including Sales, Parts, Service, Rentals, Payments, Accounting, and Customer Relationship Management (CRM). When implemented into their daily operations, Lightspeed helps dealers increase their profitability by selling more units, service, and parts, all while creating a more streamlined experience for customers.
Lightspeed is the most complete and integrated DMS in the industry with over 500 integrations with Original Equipment Manufacturers (OEMs), aftermarket parts and accessory distributors and dozens of other software tools that a dealership may use to run their business. Uniquely designed by dealers for dealers, and refined over the past 4 decades, Lightspeed empowers over 4,500\+ dealers across North America with the tools and technology they need to manage their dealerships.
We are an innovative and collaborative team looking to be at the forefront of AI\-powered software development. We're seeking Software Engineers \- AI who are passionate about leveraging artificial intelligence to accelerate development, enhance code quality, and solve complex technical challenges. This role requires professionals who embrace AI\-assisted coding workflows, prompt engineering, and modern development practices while maintaining excellence in traditional software engineering fundamentals. Ideal candidates thrive in fast\-paced environments and are excited about the intersection of AI and software development.
What you'll do:
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- Leverage AI coding assistants (Claude, GitHub Copilot, or similar) to accelerate development, refactoring, and code review processes
- Collaborate in Agile teams to refine requirements, estimate tasks, and contribute to sprint activities with AI\-enhanced productivity
- Design and implement software solutions using object\-oriented principles, ensuring reusability, scalability, and maintainability
- Develop clean, efficient, well\-documented code with AI assistance while maintaining high standards of code ownership and understanding
- Utilize AI tools for automated testing, test case generation, and maintaining comprehensive unit test coverage
- Participate in AI\-assisted code reviews, focusing on architecture decisions, logic validation, and security considerations
- Work closely with business, product management, QA, and hosting teams to facilitate smooth rollouts across AWS environments (development, staging, production)
- Manage and optimize CI/CD pipelines with AI\-enhanced automation for build, test, and deployment processes
- Apply prompt engineering techniques to maximize effectiveness of AI coding tools for your specific development tasks
- Use team tools including Jira, GitLab, SwaggerHub, Bruno, and AI platforms for project tracking, version control, API documentation, and testing
- Maintain comprehensive documentation in Confluence, leveraging AI to keep resources current and accessible
- Contribute to team knowledge sharing on AI tool best practices, effective prompts, and AI\-assisted development workflows
What you should have:
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*Required Qualifications:*
- Bachelor's degree in Computer Science, Engineering, or related field with 3\-5 years of development experience, including 1\+ years of agentic AI development experience; or equivalent experience in lieu of a degree
- Proficiency in Python and Java (3\-5 years each), with hands\-on experience in AWS cloud environments
- Demonstrated experience using agentic coding tools (Claude Code, GitHub Copilot, Cursor, or similar), including the ability to critically evaluate, validate, and debug AI\-generated code
- Strong computer science fundamentals, including data structures, algorithms, object\-oriented design, and software architecture principles
- Proven experience with Agile and Scrum methodologies in fast\-paced development environments
- Experience developing and integrating RESTful APIs with proficiency in SQL and relational databases
- Understanding of distributed systems and microservices architecture
- Working knowledge of Linux/Unix operating systems and command\-line tools
- Strong communication and collaboration skills, with a growth mindset and ability to prioritize effectively in a fast\-paced environment
*Preferred Qualifications:*
- Experience with prompt engineering and optimizing AI tool outputs for software development tasks
- Familiarity with test\-driven development (TDD) and achieving high code coverage standards, including AI\-generated test cases
- Knowledge of AI model capabilities, limitations, and best practices for AI\-assisted development
- Experience integrating front\-end and back\-end systems with modern architectural patterns
- Proficiency with modern databases (PostgreSQL preferred) and microservices architecture
- Knowledge of containerization and orchestration technologies (Docker, Kubernetes)
- Experience with infrastructure as code (Terraform) and DevOps practices
- Understanding of code quality tools, static analysis, and automated security scanning
- Contributions to open\-source projects or technical blog posts demonstrating AI\-assisted development expertise
Inclusion and Diversity at Lightspeed:
At Lightspeed, we celebrate the uniqueness of every individual and encourage diverse perspectives. We believe that inclusion drives innovation and fosters meaningful connections. We are committed to building an environment where everyone feels valued and empowered to make an impact.
Equal Employment Opportunity Statement:
Lightspeed is an Equal Opportunity Employer and is dedicated to building a diverse and inclusive workforce. All qualified applicants will be considered for employment without regard to race, color, creed, ancestry, national origin, gender, sexual orientation, gender identity, gender expression, marital status, religion, age, disability, veteran status, or any other protected category.
Important Note:
Applicants must be authorized to work in the U.S.
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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 3,708 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Lightspeed DMS, 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 $219,250 based on 424 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Lightspeed DMS AI Hiring
Lightspeed DMS has 1 open AI role right now. They're hiring across AI Software Engineer. Based in South Jordan, UT, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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