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About This Role
ProfitSolv is a SaaS business services provider for the legal and accounting industry. We are looking for an AI Software Engineer to join our growing team!
We are seeking a seasoned AI Software Engineer to support in building the next generation of AI\-powered legal SaaS platforms — transforming how firms automate workflows, analyze data, and make informed decisions. The AI Software Engineer will be responsible for transactional AI integrations, automation systems, and real\-time prompt engineering for SaaS applications.
What we provide:
- Opportunity to Invest in Your Future. We offer a 401K match.
- Paid Time Off. Enjoy paid time off and paid holidays.
- Great Coverage. Take advantage of health, dental, and vision HSA and FSA policies.
- A Great Team. Collaborate with smart, curious, hardworking individuals.
- Performance Compensation. Be rewarded for your hard work with performance\-based merits.
- Remote Work. Want to work from home? No problem!
As an AI Software Engineer, you will:
- Design and implement AI\-driven workflows that automate repetitive back\-office operations.
- Build and optimize prompt pipelines and structured output systems for dynamic UI and UX features.
- Integrate and orchestrate AI models including OpenAI, other major APIs, and local or private LLMs to deliver scalable, cost\-efficient automation.
- Collaborate closely with frontend engineers to deliver AI\-augmented React or Angular interfaces.
- Ensure cost\-efficient and scalable execution of AI workloads in high\-transaction environments.
- Drive experimentation with contextual AI features such as autocomplete, recommendations, and in\-app copilots.
- Other duties as assigned.
This position follows established policies and procedures to keep confidential information secure.
A great fit for this position has:
- Practical experience integrating and managing models from OpenAI, Anthropic, Google Vertex AI, Azure AI Services, Mistral, and private or local LLMs such as Ollama or Hugging Face.
- Experience with AWS SageMaker and Bedrock for AI model deployment and orchestration.
- Experience using AI developer tools such as GitHub Copilot, Claude Code, Codex, or Cursor.
- Experience designing and optimizing retrieval\-augmented generation systems.
- Proven success implementing AI\-powered features in large SaaS or highly transactional environments.
- Familiarity with Vercel V0 for frontend AI integration.
- Proficiency in at least one major frontend framework such as React, Next.js, or Angular.
- Strong development experience in .NET (C\#) or Node.js / TypeScript.
Additional Desirable Qualifications
- Familiarity with AWS serverless tools including Lambda, API Gateway, DynamoDB, RDS, and CloudWatch.
- Experience with CI/CD pipelines using GitHub Actions, Terraform, or Docker.
- Experience training and fine\-tuning models.
- Ability to sit for prolonged periods at a desk and work on a computer.
- Must be able to lift up to 15 pounds at times.
- Ability to handle stress
- Ability to meet work deadlines
Our commitment to you: At ProfitSolv, we are committed to being a diverse and inclusive workplace as an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, age, national origin, protected veteran status, disability status, sexual orientation, gender identity or expression, marital status, genetic information, or any other characteristic protected by law. We embrace a diverse group of backgrounds and experiences to connect with clients, solve problems, and innovate.
Work location: Remote – U.S. only
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 26,159 AI roles we're tracking, AI Software Engineer positions make up 2% of the market. At ProfitSolv, 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 $235,100 based on 665 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
ProfitSolv AI Hiring
ProfitSolv has 2 open AI roles right now. They're hiring across AI Software Engineer, Data Engineer. Based in US.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% of all AI roles offer remote work.
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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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.
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