Expert-level Python skills and strong experience building production backend systems.
Advanced knowledge of Python asynchronous programming, including asyncio, concurrency, and multiprocessing.
Strong experience with FastAPI and API architecture.
Experience building low-latency, high-throughput, or real-time systems in production.
Strong understanding of distributed and event-driven system design.
Hands-on experience with technologies such as:
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Event-driven architectures
Experience building AI agents, LLM applications, or AI orchestration platforms.
Production experience with modern AI technologies such as LangChain or equivalent frameworks, MCP, LLM APIs, and real-time communication platforms such as LiveKit or equivalent.
Strong understanding of scalable architecture, system decomposition, modularity, resilience, and fault tolerance.
Experience with observability, performance profiling, latency optimisation, load testing, and production debugging.
Strong software engineering fundamentals, including clean architecture, maintainability, and automated testing.
Comfort working with Docker, Linux, CI/CD pipelines, Kubernetes fundamentals, and at least one major cloud platform such as GCP, AWS, or Azure is a strong advantage.
AI-Native Software Engineering
You should already be using AI coding tools such as Claude Code, Codex, Cursor, Gemini CLI, or equivalent tools as part of your daily engineering workflow.
We are looking for engineers who know how to:
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Break complex engineering problems into clear, AI-executable tasks.
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Write precise technical specifications and prompts.
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Orchestrate multiple AI coding agents or workflows in parallel.
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Rapidly review, test, and validate AI-generated code.
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Identify hallucinations, security vulnerabilities, and architectural weaknesses.
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Refactor AI-generated code into clean and maintainable production software.
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Use AI to accelerate debugging, testing, documentation, and refactoring—not only initial code generation.
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Increase development output without reducing engineering quality or accountability.
Using AI tools is not a substitute for strong engineering fundamentals. You must be able to understand, challenge, and take full ownership of everything that enters production.
Comfort working closely with AI researchers, product teams, and infrastructure engineers to turn experimental capabilities into reliable production systems.