Sr. AI Software Engineer (Remote)

Inspira Financial•Oak Brook, IL

7d•Remote

About The Position

The Sr. AI Software Engineer reports to the Sr. Director, AI Center of Excellence, in the Technology Department and has no direct reports. The role embeds with business teams across Health & Benefits, Retirement & Wealth, operations, contact center, finance, HR, marketing, sales, and engineering to identify AI opportunities, define requirements, build prototypes, and deliver governed, measurable AI agents into production. The engineer brings business needs and learnings back to the AI Center of Excellence while delivering value to business partners. The position focuses on finding and scoping business problems suited to AI; building production agents and automations on approved enterprise platforms; measuring and reporting ROI; applying AI governance and compliance controls; and turning recurring work into reusable patterns. It also helps raise AI fluency among supported business teams.

Requirements

Bachelor of Science degree in Engineering or Computer Science.
3–6 years of software engineering experience shipping and supporting production systems.
At least 2 years of hands-on experience building AI solutions across assistants, agents, workflow automation, retrieval, and document processing.
Demonstrated end-to-end ownership of taking a business idea from concept through requirements, prototype, and production use.
Ability to define and measure business value, construct a baseline, defend a measurement method, and communicate cost and benefit independently.
Working knowledge of at least one AI governance or management framework, such as ISO/IEC 42001 or NIST AI RMF, and ability to convert it into implemented controls.
Experience instrumenting telemetry as part of the build process rather than after an incident.
Ability to work effectively within unfamiliar business functions, build credibility quickly, influence stakeholders, and build relationships.
Ability to learn and adapt to a changing AI platform and tooling landscape.
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.

Nice To Haves

AI continuous-learning certifications.
Financial services, healthcare, benefits administration, or other regulated industry experience, particularly environments handling PII, PHI, or fiduciary data.
Process improvement experience, such as Lean, Six Sigma, or process mining.
Fluency in low-code automation platforms alongside code-first development.
Experience with contact center, claims, or back-office operations technology.
Experience developing internal enablement materials, documentation, or practitioner communities.

Responsibilities

Embed within business teams to identify repetitive, automatable, or bottlenecked work.
Translate business needs into written solution statements, success criteria, data requirements, and documented recommendations about whether AI is appropriate.
Build working prototypes to validate feasibility before committing to a full build.
Scope opportunities and select an appropriate solution, such as a single-purpose agent, automated workflow, multi-tier agent system, or approved vendor capability.
Build production agents and automations on approved enterprise platforms, including assistant and agent frameworks, low-code workflow automation, document intelligence, conversational and agent-assist capabilities, and natural language analytics.
Design multi-tier agent systems with orchestration, specialized agents, scoped tools, defined boundaries, and human handoffs.
Integrate with business systems through approved connectors and MCP servers, and support approval processes for new connectors when needed.
Hand off completed builds with documentation, a runbook, a named owner, and a business user able to operate the solution independently.
Baseline process performance before builds, capturing applicable measures such as cycle time, volume, error and rework rates, handle time, and cost per unit.
Instrument deployed agents to measure value continuously.
Report outcomes honestly, including unsuccessful builds and lessons learned.
Apply an ISO/IEC 42001 governance mindset to each build, including defined purpose, risk classification, data handling, human oversight, and evidence that controls operated as intended.
Define and implement governance, security, and compliance telemetry for agents, covering tool calls, scope adherence, escalation and override rates, data access, output quality, and cost.
Build monitoring and alerting to surface drift, boundary violations, and unexpected spend to an owner.
Operate within the AI Governance Committee process and the AI Acceptable Use Policy.
Report business team needs, governance friction points, recurring patterns, and tooling gaps to the AI Center of Excellence.
Convert repeat builds into reusable patterns, templates, and starter kits for consultants and delivery teams.
Run enablement sessions and working labs to raise AI fluency among supported business teams.
Perform other duties as assigned.