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About This Role
Why Work Here
- Headquarters is in Charlotte, NC
- Recently expanded to nearshore
- isolved ranked for SMB Payroll in 2023 Sapient Report
- Voted top places to work in USA 2023
Job Description
The isolved Senior Software Engineer, AI Platform role owns both program execution and technical direction, leading ~20 engineers across domain teams (Tax, Benefits, Time, Payroll, Shared Logic), alongside two Engineering Managers and a Data Architect. The position blends delivery leadership with deep technical involvement, serving as a key decision-maker, escalation point for complex challenges, and ultimate owner of program outcomes.
Core Job Duties
- Design and build a scalable LLM gateway with model routing, prompt management, cost attribution, rate limiting, and caching
- Develop and operate RAG pipelines, embedding services, and vector search infrastructure for platform-wide use
- Implement platform-level cost optimization strategies, including semantic caching and model selection by workload
- Build and maintain agentic runtime infrastructure, including orchestration, state management, and human-in-the-loop patterns
- Develop extensible MCP server and tool ecosystems for product team integration
- Design and support multi-agent coordination patterns using modern frameworks and protocols
- Establish comprehensive AI observability, including usage, latency, cost tracking, and distributed tracing
- Implement AI governance controls, including access management, audit logging, content filtering, and security protections
- Build AI incident detection and response capabilities, including monitoring for failures, hallucinations, and cost anomalies
- Create developer-friendly SDKs across languages (Python, .NET, TypeScript) to simplify platform adoption
- Define "paved road" patterns for common AI use cases and support onboarding of product teams
- Build automated evaluation pipelines and continuously monitor production quality and model performance
Minimum Qualifications
- + 5+ years of professional software engineering experience, with Python as your primary language + 2+ years building production LLM-powered systems - inference, RAG, agentic patterns, or AI infrastructure + Deep Python expertise - this is the primary language for AI platform work + Working proficiency in C#/.NET - the platform serves teams that live in C#, so interop is real and matters + Strong hands-on experience with agentic frameworks - Semantic Kernel, LangGraph, LangChain, or you've built your own + Production experience with RAG architecture: chunking strategies, embedding models, vector search, retrieval quality, and the failure modes that don't show up in demos + Azure AI Foundry / Azure OpenAI experience - model deployment, API integration, observability tooling + Experience building internal platforms or SDKs that other engineers depend on - you understand what makes a platform feel good to use + Strong grasp of AI observability: token usage, latency, cost tracking, and distributed tracing across multi-agent workflows + Preferred: + - Experience with TypeScript and building developer SDKs or tooling
- Hands-on experience with AI evaluation frameworks (LLM-as-judge, automated regression testing)
- Knowledge of AI governance practices, including access control, audit logging, and security safeguards
- Familiarity with container-based deployments (e.g., Azure Container Apps) and infrastructure-as-code (Terraform)
- Awareness of AI regulatory frameworks such as NIST AI RMF or ISO/IEC 42001
About isolved
isolved is a leading provider of human capital management (HCM) solutions that combines modern technology with expert services and support. Purpose-built for People Heroes™, isolved gives HR, payroll and benefits leaders the tools and insights to streamline operations and deliver employee experiences that matter. isolved People Cloud™ is a connected HCM platform with built-in artificial intelligence (AI) and analytics that brings together HR, payroll, benefits, workforce management and talent management in one experience. Built on a legacy of 40 years in the market, isolved is trusted by more than 200,000 employers and used by 9 million U.S. employees, representing about one in 20 American workers. Visit www.isolvedhcm.com.
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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 9,079 AI roles we're tracking, AI Software Engineer positions make up 5% of the market. At isolved, 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. Senior-level AI roles across all categories have a median of $229,300.
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.
isolved AI Hiring
isolved 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 $209,100 across 306 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 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.
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