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Aspire Software logo
Aspire Software

We never stop building. A vertical acquisition software company that owns, operates and manages a diverse portfolio.

AI-Native Software Engineer

Full-stack EngineerSoftware EngineerFull TimeRemoteSeniorTeam 1,001-5,000H1B No SponsorCompany SiteLinkedIn

Location

Canada

Posted

73 days ago

Salary

0

Seniority

Senior

Job Description

AI-Native Software Engineer

Aspire Software

• Architect and Build Scalable Systems • Design and implement high-quality backend systems in .NET and C#. • Build modular, testable, maintainable architectures. • Make sound trade-offs between performance, maintainability, and speed of delivery. • Refactor legacy systems intelligently (not cosmetically). • Leverage AI Across the SDLC • Use LLMs and code-generation tools to: - Draft and refactor production code - Generate unit/integration tests - Create migration scripts - Perform static analysis and code reviews - Produce technical documentation • Build internal AI assistants for: - Log analysis - Support ticket triage - Codebase navigation - Technical debt discovery • Continuously optimize development workflows using AI. • Own Architecture Decisions • Design APIs (REST, event-driven, microservices where appropriate). • Work with cloud-native patterns (Azure preferred). • Integrate AI services into production systems. • Evaluate when to build vs. buy vs. automate. • Raise the Bar • Set standards for AI-assisted development. • Mentor other engineers in AI-native workflows. • Push for measurable productivity gains. • Eliminate manual processes wherever possible.

Job Requirements

  • Strong experience with:
  • .NET
  • C#
  • ASP.NET Core / .NET Web APIs
  • Solid understanding of:
  • Entity Framework / ORM patterns
  • SQL and database design
  • Clean architecture principles
  • Dependency injection
  • Testing frameworks (xUnit, NUnit, etc.)
  • Azure experience preferred
  • CI/CD pipelines
  • Infrastructure as Code familiarity
  • Observability (logging, tracing, monitoring)
  • Hands-on experience with:
  • LLM APIs
  • Code copilots and AI IDE tooling
  • Prompt engineering for engineering workflows
  • Experience building:
  • AI-assisted tools
  • Internal bots or automation agents
  • Understanding of:
  • Model limitations
  • Cost-performance trade-offs
  • Guardrails and reliability

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