Software Engineer III

Software Engineer III at Assurity Life Insurance Company designs and delivers application, platform, or system capabilities that support mid-to-large AI and automation solutions integrated with core enterprise systems. The role emphasizes production quality, security, reliability, and coaching teammates while frequently directing AI agents and leading end-to-end initiatives.

Location and Work Arrangement

• Location: Lincoln, NE (onsite)
• Remote: This position is remote, with occasional on-site attendance. Specific travel arrangements and collaboration hours are communicated for the opening.

Responsibilities

• Act as design lead for an assigned application, platform, or system, selecting components and design patterns, evaluating performance and reliability considerations, and ensuring the solution remains maintainable, secure, stable, and fit for purpose.
• Plan the technical direction for current delivery and the near-term roadmap, define service contracts and data ownership within the assigned area with the Solutions Architect, and record key decisions.
• Design components and integrations within the assigned system and document design decisions and implementation guidance to a level that supports efficient development.
• Participate frequently in solution design, reviews, and troubleshooting throughout delivery.
• Run at least one small-to-mid initiative, or a mid-to-large AI or automation solution, from intake through specification, design, build, UAT, and rollout, including performing analysis without waiting for an analyst assignment.
• Write user stories and acceptance criteria, confirm them with business users, and verify completed work directly with stakeholders.
• Direct AI agents to build and harden features, fixes, and solutions in the real codebase, progressing from a working version used by users through production release, including integration with SQL Server data sources, the event backbone, and enterprise services; write code by hand when needed for task fit, platform requirements, or quality.
• Integrate data from back-end services and databases and maintain, expand, and scale enterprise-level software.
• Lead verification of AI-generated changes by establishing verification strategies and review practices, identifying recurring failure modes, and reviewing other engineers’ changes for correctness, security, performance, and maintainability.
• Write unit and integration tests and evaluate agent-produced changes using testing, debugging, and review methods appropriate to risk prior to release.
• Implement human review, audit, access, and egress controls and set approval boundaries for agents within own services.
• Create and maintain software documentation, including repository instructions, reusable prompts, and validation methods that help teammates and agents produce higher-quality work.
• Promote changes through CI/CD release pipelines (dev, test, stage, and production), and follow changes through to production and real use.
• Design monitoring, drift signals, guardrails, and alerting.
• Participate in on-call, troubleshoot production issues, lead incident resolution and follow-up, and respond to user questions and requests in a timely manner.
• Assist at least two teammates in AI fluency (including Software Engineer I and II), with evidence in their portfolios, and review their highest-risk changes.
• Design reusable agent workflows for larger work, select orchestration only when useful, and teach others how to delegate effectively.
• Share system design context and patterns so teammates and agents can work effectively, and convert proven agent-directed delivery patterns into shared team practice.
• Partner with engineers, business systems analysts, product owners, project managers, and operations; estimate and log time daily; provide project status to management and end users; and present demonstrations of project functionality.
• Maintain skills through ongoing training and study, including AI fluency courses, and evaluate emerging trends and AI capabilities for fit with the system’s roadmap.

Required Qualifications

• Typically 4 or more years of professional software development experience, including owning an initiative end to end.
• A sustained portfolio of work shipped iteratively with AI agents, including reusable workflows and verification practices used by others, with sound judgment in verifying output. The interview includes a live build exercise.
• Experience designing application components, integrations, and system architecture, including design patterns, performance, and reliability, and integrating with core business systems and event-driven services.
• Experience leading small-to-mid initiatives from intake through rollout, including writing stories and acceptance criteria, working directly with business users to confirm completed work, and experience coaching or mentoring other engineers.
• Strong experience with relational and document or graph data systems, object-oriented programming, web application development, REST APIs, and production agent or LLM systems.
• Experience building interfaces with modern HTML/CSS and building functional solutions using more than one of: Vue, Angular, JavaScript, C#, TypeScript, Python, .NET MVC, Entity Framework, or REST.
• Ability to design and implement unit and integration tests and lead troubleshooting of complex issues with modern IDEs (preferably Visual Studio), plus experience with modern source control and CI/CD release pipelines (preferably Azure DevOps and Git), and experience with Linux and container operations. Experience with modern data formats, preferably JSON and XML, and Agile processes.
• Strong problem diagnosis and troubleshooting skills, creative problem-solving, professional written and verbal communication with technical and business partners, and organizational skills to manage multiple tasks within timelines and budgets with business acumen.
• Ability to thrive in a fast-paced remote team environment and learn rapidly to master diverse technologies and techniques.
• Talent aligned with values and working style expectations including values, work intensity, achiever, positivity, resourcefulness, command, relationship-extension, and exactness.

How We Want to Work

• Act and decide: Make reversible calls quickly and time-box remaining work to hours or days, own decisions, and keep stakeholders informed. Slow down deliberately for one-way doors such as production data changes, real customer or agent communications, money movement, compliance positions, and security boundaries.
• Show, don’t tell: Bring working examples rather than slides or specifications.
• Hold work loosely: Treat proofs of concept as questions for users and rework or discard based on feedback.
• Own the outcome: Follow changes through to production and real use, monitor and audit logs, and fix issues.
• Leave a trail: Keep generated requirements, decision notes, tests, and agent context files current so others can pick up work.

AI Fluency

• Direction as default: Direct AI agents as the primary method of producing work, choosing another approach when quality, security, or platform requirements require it. Maintain responsibility for correctness, security, and quality of delivered outcomes.
• Delegation: Routinely direct agents on substantial implementation, testing, and documentation work where suited, and design reusable workflows for larger work. Select orchestration only when useful, divide work sensibly, explain significant exceptions, recover when an agent becomes unproductive, and teach others how to delegate effectively.
• Description: Provide clear intent, context, constraints, and completion criteria; refine as new information emerges; and keep repository context files current as code changes.
• Discernment: Verify agent output by running it, testing it, and reviewing before release; explain results and limits; establish verification strategies and review practices, identify recurring failure modes, improve team quality, redirect or stop ineffective work, and own defects.
• Diligence: Follow approved tool, data, and access rules independently, protect secrets and sensitive data, record material AI contributions, and raise control gaps promptly.
• Leverage: Apply repeatable agent workflows to recurring engineering work and demonstrate credible benefit, including verification and rework.

Technologies

• AI, automation
• SQL Server
• CI/CD
• Azure DevOps
• Git
• Linux
• containers
• Visual Studio
• HTML, CSS
• Vue, Angular, JavaScript, C#, TypeScript, Python, .NET MVC, Entity Framework
• REST APIs
• JSON, XML
• unit and integration tests, UAT
• on-call

On-Call and Schedule Requirements

• Consistent and reliable attendance is an essential function. Work may exceed 40 hours per week.
• On-call rotation: Participate in the Software Development team’s rotating on-call schedule after training, including nights and weekends. Remain reachable and respond promptly to production alerts within defined response times and escalation paths.
• Contain first: While on call, be empowered to take immediate containing actions such as rolling back a release, pausing outbound communications, parking workflows, or disabling a feature. Fix forward during business hours unless impact requires otherwise, and record a brief incident note.
• Planned off-hours work: Occasional scheduled evening or weekend work for production deploys, cutovers, data migrations, and go-lives, with advance notice.

Preferred Experience

• Claude Code or similar agentic tools; Python/FastAPI; C#, TypeScript, Next.js, and React; n8n; MCP.
• Azure DevOps or Jira; GitHub Actions; Podman or Docker; Azure; PostgreSQL and SQL Server; Kafka or Redpanda event streaming; Neo4j; Grafana and Prometheus; LDAP/Active Directory; application monitoring, performance tuning, or security design practices.
• Insurance or financial services systems and enterprise integration experience.

Geographic Eligibility Note

We are not currently considering applicants in CA, CO, DC, HI, IL, MA, MD, MN, NJ, NY, VT, WA for this position.