Lead AI Software Engineer (Remote)

Inspira Financial is hiring a Lead AI Software Engineer to build and operate its enterprise AI engineering platform and deliver production AI services across the business. The role also partners with AI-Ops and control functions, scales agentic systems, and mentors engineering teams.

Responsibilities:

• 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
• 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
• 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
• Build secure, compliant AI integration patterns appropriate for a regulated financial services environment
• Build and help scale agentic pipelines and multi-agent orchestration across the software development lifecycle, from pilot to 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
• 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
• 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
• Identify and prototype emerging AI capabilities relevant to the business and technology roadmap, with a clear building-block approach for scaling
• Build fast, focused feasibility spikes and use them to build foundational components that scale with teams through clean handoff
• Lead applied continuous learning sessions and pair directly with engineering teams on new development methods and prototype or pattern adoption
• Coach embedded AI consultants and the coaching network on CoE patterns and standards to scale AI delivery capacity beyond the CoE
• 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
• Partner with security, risk, compliance, and data governance teams to support safe and compliant AI delivery

Requirements:

• Bachelor of Science in Computer Science
• 7+ years building and operating production software, including 2+ years delivering production AI, LLM, or ML systems, with a track record of building and shipping components of an enterprise-scale platform
• Building and operating production agentic workflows as state machines, including branching logic, retry and recovery paths, and precise control over multi-step process flow
• Multi-agent system design, including role assignment and coordination, agent-to-agent conversation patterns, and code execution loops for iterative problem solving
• Deep fluency with AI-assisted development environments such as GitHub Copilot, Claude Code, or ONA, with experience helping teams adopt them and measure their impact
• 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
• API and integration experience across REST, event-driven, and MCP patterns, including integrating AI into systems of record and workflow platforms
• 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 self-hosted or open-weight models, inference optimization, and gateway or proxy patterns in front of multiple models
• 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
• Data engineering foundations for AI, including document ingestion and extraction pipelines, data source integration and mapping, embeddings, and data quality controls
• Technical leadership without formal authority, including contributing to architecture decision records and design reviews, mentoring early-career and senior engineers, and communicating trade-offs clearly
• Ability to deliver roadmap capabilities, 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 finding a critical path and managing stakeholders
• 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
• Document intelligence and intelligent document processing experience preferred, including OCR, extraction, and classification pipelines with measured accuracy targets
• Experience with decisioning and adjudication systems where machine decisions are reviewed, audited, and held to an accuracy and turnaround standard is a plus
• Conversational and voice AI experience preferred, including chat deflection, speech-to-text, call transcription and summarization, and authenticated, account-specific responses is a plus
• Experience with fraud, anomaly, and identity signal detection preferred, including behavioral baselines, relationship or graph analytics, and detection of doctored or AI-generated media is a plus
• Experience with workflow and low-code automation platforms preferred