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We never stop building. A vertical acquisition software company that owns, operates and manages a diverse portfolio.
AI-Native Software Engineer
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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