Asks for

The ad says“Advanced knowledge of Python asynchronous programming, including asyncio, concurrency, and multiprocessing.”
The ad says“Advanced knowledge of Python asynchronous programming, including asyncio, concurrency, and multiprocessing.”
The ad says“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.”
The ad says“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.”
The ad says“Redis and caching”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“Advanced knowledge of Python asynchronous programming, including asyncio, concurrency, and multiprocessing.”
The ad says“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.”
The ad says“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.”
The ad says“Event-driven architectures”
The ad says“Strong experience with FastAPI and API architecture.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“Message queues”
The ad says“Advanced knowledge of Python asynchronous programming, including asyncio, concurrency, and multiprocessing.”
The ad says“Parallel processing”
The ad says“Expert-level Python skills and strong experience building production backend systems.”
The ad says“Redis and caching”
The ad says“Streaming systems”
The ad says“WebSockets”

The Role

We’re building the next generation of real-time conversational AI systems.

Our agents operate under real production traffic, where latency, scalability, reliability, and engineering quality are critical. We’re looking for a Senior AI Software Engineer to design and build the production systems that allow these agents to respond intelligently and reliably in real time.

This is not an R&D or research role.

This role sits at the intersection of AI engineering, backend systems, and distributed real-time architecture.

You will take ownership of complex production systems behind our AI agents, including low-latency backend services, event-driven pipelines, agent orchestration, and the infrastructure patterns required to operate them at scale.

We also believe that modern software engineering is changing. The strongest engineers are no longer measured only by how much code they write manually, but by how effectively they can design systems, orchestrate AI coding agents, and validate AI-generated work without compromising quality.

What You’ll Do

Design, build, and operate production-grade backend systems powering our conversational AI platform.
Build low-latency, high-throughput services using Python, FastAPI, and asynchronous programming.
Develop real-time, event-driven systems using WebSockets, streaming technologies, queues, and parallel processing.
Design distributed architectures that remain reliable and responsive under significant production traffic.
Build and improve AI agent orchestration systems and LLM-powered applications.
Integrate LLM APIs, AI frameworks, communication infrastructure, and external AI providers into the platform.
Use technologies such as Redis and caching layers to improve latency, throughput, and system efficiency.
Profile production systems, identify performance bottlenecks, and optimise critical execution paths.
Improve system resilience, fault tolerance, observability, and production debugging capabilities.
Write comprehensive unit, integration, and load tests.
Design clean, modular, and maintainable architectures that can evolve as the product scales.
Work closely with AI, product, and engineering teams to translate agent capabilities into reliable production systems.
Use AI coding tools to accelerate development, debugging, testing, refactoring, and technical exploration.
Review and validate AI-generated code for architectural flaws, hallucinations, security issues, and maintainability risks.
Write a significant amount of production code while improving the overall engineering quality of the platform.

Requirements

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:

WebSockets
Streaming systems
Message queues
Redis and caching
Parallel processing
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:

Break complex engineering problems into clear, AI-executable tasks.
Write precise technical specifications and prompts.
Orchestrate multiple AI coding agents or workflows in parallel.
Rapidly review, test, and validate AI-generated code.
Identify hallucinations, security vulnerabilities, and architectural weaknesses.
Refactor AI-generated code into clean and maintainable production software.
Use AI to accelerate debugging, testing, documentation, and refactoring—not only initial code generation.
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.

Eligibility to work in France is required.

Benefits

What we offer

High ownership and autonomy
Diverse, international team across Europe
Exposure to cutting-edge AI, voice, and enterprise deployments
Fast-paced environment with strong learning curve
25 days holiday + public holidays
Private healthcare with 50% coverage for you, and 100% coverage for your kids
Swile
Pension contribution
ESOP/VSOP (shares)