Software Development Engineer - (Temporary)
$19,000–$24,000 year
Remote
Job Summary
Build and maintain production-grade Python backend services for AI-powered applications, integrating LLM APIs into real-world workflows with prompt handling, response validation, and retries. Design scalable REST APIs, async workflows, and background job systems while debugging complex issues across distributed services and data pipelines. Monitor system performance using telemetry tools, automate recurring data processing workflows, and collaborate with cross-functional teams to transform product ideas into production-ready features. Contribute to architecture decisions regarding scalability and safe AI system usage, ensuring clean, testable code within a multi-language codebase.
Required Qualifications
- Bachelor's degree in Computer Science or a related field
- 3+ years of professional experience in backend or systems engineering
- Strong experience in Python development for production systems
- Experience integrating LLM APIs or AI-driven services into real applications
- Proven ability to work within large codebases and collaborative environments
- Python (strong)
- backend system design
- API development
- Async workflows
- job queues
- service orchestration
- Debugging across APIs, services, and data pipelines
- Python
- C#
- C/C++
- JavaScript/TypeScript
- SQL
- .NET Web API
- ASP.NET (MVC & WebForms)
- Entity Framework
- React
- Bootstrap
- Ajax
- Testing frameworks (xUnit or similar)
- Prompt design
- response parsing
- guardrails
- Model integration patterns
- failure handling
- Understanding of LLM limitations
- Understanding of production constraints
- Azure DevOps
- Git
- Docker
- Terraform
- SQL Server or similar databases
- Elastic Stack
- Postman
- Swagger
- AWS or Azure cloud environments
- Microservices
- distributed systems
- modular architectures
- Windows
- Linux
- Make
- Ninja (preferred)
Desired Qualifications
- Experience with RAG
- embeddings
- vector databases
- retrieval systems
- Familiarity with agent workflows
- multi-step orchestration
- Experience with model monitoring
- prompt versioning
- evaluation systems
- Understanding of AI safety
- privacy
- secure data handling
- Exposure to cloud-native deployments
- scalable infrastructure
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