AI-Native Software Engineer
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Job Description
AI-Native Software Engineer focusing on AI-native workflows and scalable backend systems for Anju Software, a life sciences solutions provider. Building production-ready systems using .NET/C# and AI tools.
Responsibilities:
- 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.
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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