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About This Role
Position Summary: The AI CoE Principal Software Engineer will report to the Sr. Director, AI Center of Excellence, in the Technology Department. This role is the most senior individual contributor on the team and the technical owner of the enterprise AI engineering foundation, including the semantic router, model and harness hosting, the enterprise AI gateway, and the AI operations layer used across the business. The role is responsible for setting AI engineering architecture and standards and then building them, carrying a multi-year capability roadmap from discovery through prototype, validation, and production scale. Primary duties include designing and building the shared AI platform and semantic routing layer; product inference and personalization, partnering with AI-Ops on monitoring, evaluation, and governance guardrails; building secure, compliant AI integration patterns for a regulated financial services and health and benefits environments; leading agentic pipeline and multi-agent orchestration work; delivering production AI services for CoE-enabled business functions; developing reusable reference architectures for other engineering teams; managing AI unit economics; prototyping emerging AI capabilities; mentoring senior and lead engineers; and partnering with security, risk, compliance, and data governance. This role does not have direct reports. Duties & Responsibilities:
- Architect, build, and operate the shared enterprise AI platform, including core semantic data, model, harness, and hosting capability used across developer and in-product personalization use cases.
- Design and maintain intelligent request routing using semantic analysis and deterministic logic to direct requests to the best-fit LLM, internal tool, or agentic pool, using cost, latency, and privacy as routing criteria.
- Partner with AI-Ops to enable metric-based reporting, quality, performance, and drift monitoring across live systems, build model evaluation harnesses, and establish governance guardrails for safe AI use at scale.
- Design and implement secure, compliant AI integration patterns appropriate for a regulated financial services environment.
- Build and scale agentic pipelines and multi-agent orchestration across the software development lifecycle, from pilot through measured engineering capacity across product teams.
- Deliver production AI services supporting document intelligence and claims adjudication, conversational and voice self-service, call intelligence, fraud and anomaly detection, and regulatory monitoring.
- Develop reusable reference architectures and standard patterns so other engineering teams and embedded consultants can build AI solutions without rebuilding the foundation.
- Evaluate unit economics for AI workloads, including cost per transaction, token and inference spend, latency budgets, and build-versus-buy and self-host-versus-vendor decisions.
- Identify and prototype emerging AI capabilities relevant to the business and technology roadmap, including fast, focused feasibility spikes with clear recommendations.
- Lead hands-on training sessions and pair directly with engineering teams on new development methods and prototype or pattern adoption.
- Monitor the AI landscape and translate findings into roadmap recommendations on what to pilot, what to watch, and what to decline.
- Set technical direction and standards for embedded AI consultants and the coaching network to scale AI delivery capacity beyond the CoE.
- Mentor senior and lead engineers across business units, review AI designs, and serve as the escalation point for complex agentic and integration issues.
- Partner with security, risk, compliance, and data governance teams to support safe and compliant AI delivery.
- Perform other duties as assigned.
Requirements: Education & Experience: Minimum required:
- Bachelor of Science Degree in Computer Science.
- AI Practitioner Certifications are a plus
- 12+ years building and operating production software, including 4+ years delivering production AI/ML systems and 2+ years of LLM or agentic systems in production, with a track record as the most senior technical owner of an enterprise-scale platform.
Skills & Abilities:
- Architecting and operating production agentic workflows as state machines, including branching logic, retry and recovery paths, and precise control over multi-step process flow.
- Deep fluency with AI-assisted development environments such as GitHub Copilot, Claude Code, or ONA, with a track record of driving adoption and measured impact across engineering teams.
- Semantic routing and model orchestration, including embedding-based intent analysis, deterministic fallback logic, and policy-based model selection across multiple providers on cost, latency, and privacy criteria.
- Observability and AI Ops for non-deterministic systems, including distributed tracing, behavioral analysis, usage and quality metrics, drift detection, and cost and latency telemetry for executive reporting.
- Security-aware engineering with demonstrated experience handling PII and sensitive data, including encryption, secrets management, least-privilege access, and audit trails in a regulated environment.
- Model hosting and serving experience, including deploying and operating commercial and open-weight models behind a gateway or proxy, with sound self-host-versus-vendor judgment.
- Evaluation and reliability engineering for probabilistic systems, including eval harnesses, golden datasets, offline and online scoring, guardrails, red-teaming, hallucination mitigation, and human-in-the-loop review design.
- Technical leadership without formal authority, including architecture decision records, design reviews, mentoring senior and staff engineers, and communicating trade-offs to executive stakeholders.
- Ability to own a multi-year capability roadmap, moving work from discovery through prototype, production validation, and scale against defined business metrics.
- Ability to move fluidly between abstract system thinking and tactical, incremental delivery while managing stakeholders and delivering against a critical path.
- Financial services, healthcare benefits, or other regulated industry experience preferred, with HSA/FSA, claims, custody, or payments domain exposure especially valuable.
- Knowledge of AI governance and compliance frameworks preferred, such as NIST AI RMF, ISO/IEC 42001, model risk management, SOC 2, or HIPAA control environments.
- Experience with rapid prototyping and feasibility assessment in an R&D or innovation capacity preferred.
- Experience facilitating technical learning sessions or pairing with engineers at varying skill levels preferred.
- Multi-agent system design experience preferred, including role assignment and coordination, agent-to-agent conversation patterns, and code execution loops for iterative problem solving.
- API and integration experience across REST, event-driven, and MCP patterns preferred, including integrating AI into systems of record and workflow platforms.
Other Requirements:
- Ability to occasionally work outside normal business hours to meet project deadlines as needed.
- Prolonged periods of sitting at a desk and working on a computer.
Req Benefits: The compensation for this position will vary depending on factors such as your location, skills and experience. The compensation package may also include incentive and bonus opportunities.
Inspira provides industry-leading benefits including, but not limited to: healthcare, 401K savings plan, company holidays, paid time off, parental leave and an employee assistance program.
Compensation: $152,700-$205,000 per year
Additional Details : Take the next step in your journey at Inspira Financial. Guided by our mission to enable better health and greater wealth, we help businesses and individuals thrive today, tomorrow, and into retirement by strengthening and simplifying the health and wealth journey. Serving more than 7 million clients and representing over $62 billion in assets, Inspira partners with thousands of employers, plan sponsors, recordkeepers, TPAs, and other institutional partners to help the people they support plan, save, and invest for a brighter future. Through solutions including automatic rollover services, health savings accounts, emergency savings, and custody services, Inspira is focused on delivering better outcomes for all. Learn more at inspirafinancial.com. Our values shape how we show up every day. We Lead with Heart by acting with compassion, empathy, and doing what's right; Cultivate Trust through integrity, transparency, and accountability; Aim Higher by never settling for what's expected and continually raising the standard; and Win Together by collaborating inclusively to achieve the best outcomes. Our growth and culture haven't gone unnoticed - we've been recognized on Crain's Fast 50 and Inc. 5000 for remarkable growth, and named to Built In's 2025 Best Places to Work and Gallagher's 2022 Best-In-Class Employer for our workplace culture and benefits. Don't meet every single requirement? That's okay. We know growth comes from people who are willing to learn and challenge themselves, not just check every box. If you're energized and inspired by meaningful work, continuous development, and being part of a team that turns collective effort into real impact, we want to hear from you. We look forward to receiving your application. Our office locations are designed to bring people together, foster collaboration, and build meaningful connections. While some roles are based at one of our office locations, remote status and role locations are subject to change, and relocation is not provided.
Salary Context
This $152K-$205K range is below the median for AI Software Engineer roles in our dataset (median: $192K across 198 roles with salary data).
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 Inspira 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. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($178K) sits 19% below the category median. Disclosed range: $152K to $205K.
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.
Inspira Financial AI Hiring
Inspira Financial has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Oak Brook, IL, US. Compensation range: $205K - $205K.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% 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 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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