Lead AI Software Engineer (Remote)

Inspira Financial; Oak Brook (IL); Remote; USD 180,000 - 230,000; Full time

Job summary

Inspira Financial is seeking a Lead AI Software Engineer to build and operate our enterprise AI platform, including semantic routing, model hosting, and AI governance. You will collaborate with AI-Ops and security teams to ensure compliant delivery in a regulated financial services environment, while mentoring engineers and guiding cross‑functional projects.

You will lead the design and delivery of scalable AI capabilities, guide architecture decisions, and partner with product teams to enable

Qualifications

• Bachelor's degree in CS or related field or equivalent practical experience.
• Strong background in software engineering for AI/ML systems.
• Experience mentoring engineers and collaborating across teams.
• Familiarity with security, risk, and data governance in regulated environments.

Responsibilities

• Build and operate enterprise AI platform components including data, model, and hosting layers.
• Develop semantic routing to select optimal LLMs and tools based on cost, latency, and privacy.
• Partner with AI-Ops for monitoring, drift detection, and governance guardrails.
• Create secure, compliant AI integration patterns for a regulated financial services setting.
• Scale agentic pipelines and multi-agent orchestration across the SDLC.
• Deliver production AI services for document intelligence, claims adjudication, and analytics.
• Mentor engineers across CoE and partner teams and participate in design reviews.

Skills

AI software engineering

System architecture

Mentoring

Team collaboration

Cloud platforms

Education

Bachelor's degree in Computer Science or related field

Tools

LLM platforms

Model hosting

Job description

The Lead AI Software Engineer will report to the Sr. Director, AI Center of Excellence, in the Technology Department. This role is a hands-on builder across 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 builds to the architecture and standards set by CoE technical leadership and contributes design input to help shape them. Primary duties include building and operating the shared AI platform and semantic routing layer; partnering with AI-Ops on monitoring, evaluation, and governance guardrails; building secure, compliant AI integration patterns for a regulated financial services environment; building and scaling agentic pipelines and multi-agent orchestration; delivering production AI services for CoE-enabled business functions; building reusable components to the CoE's reference architectures; evaluating AI workload unit economics; prototyping emerging AI capabilities; mentoring engineers across the CoE and partner teams; and partnering with security, risk, compliance, and data governance. This role does not have direct reports.

1. Build and operate the shared AI platform for the enterprise, including core semantic data, model, harness, and hosting capabilities used across developer and in-product personalization use cases.
2. Build 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.
3. Partner with AI-Ops to enable metric-based reporting, quality, performance, and drift monitoring across live systems, build model evaluation harnesses, and support governance guardrails for safe AI use at scale.
4. Build secure, compliant AI integration patterns appropriate for a regulated financial services environment.
5. Build and help scale agentic pipelines and multi-agent orchestration across the software development lifecycle, from pilot to measured engineering capacity across product teams.
6. Deliver production AI services supporting document intelligence and claims adjudication, conversational and voice self-service, call intelligence, fraud and anomaly detection, and regulatory monitoring.
7. Build reusable components and standard patterns aligned to the CoE's reference architectures so other teams and embedded consultants can build AI solutions without rebuilding the foundation.
8. Evaluate unit economics for AI workloads, including cost per transaction, token and inference spend, latency budgets, and analysis supporting build-versus-buy and self-host-versus-vendor decisions.
9. Identify and prototype emerging AI capabilities relevant to the business and technology roadmap, with a clear building-block approach for scaling.
10. Build fast, focused feasibility spikes and use them to build foundational components that scale with teams through clean handoff.
11. Lead applied continuous learning sessions and pair directly with engineering teams on new development methods and prototype or pattern adoption.
12. Coach embedded AI consultants and the coaching network on CoE patterns and standards to scale AI delivery capacity beyond the CoE.
13. Mentor Software Engineer I, II, and Senior-level engineers across the CoE and partner teams, participate in AI design reviews, and help resolve complex agentic and integration problems alongside Principal-level engineers.
14. Partner with security, risk, compliance, and data governance teams to support safe and compliant AI delivery.