Senior AI Software Engineer

GetVocal AI

Senior AI Software Engineer designing and building scalable backend systems for conversational AI. Responsible for production systems, ensuring reliability and performance under traffic.

Posted 7/27/2026full-timeParis • 🇫🇷 FranceSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expert-level Python programming skills and advanced knowledge of asynchronous programming, with a strong focus on building low-latency, high-throughput backend systems. Proficient in designing distributed architectures and integrating AI technologies to enhance system performance and reliability.

Highest-signal resume keywords

Expert-Level Python SkillsFastAPI and API ArchitectureAsynchronous ProgrammingDistributed System DesignAI Agent Development

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Hard Skills

PythonAsynchronous ProgrammingFastAPIWebSocketsStreaming TechnologiesMessage QueuesRedisParallel ProcessingEvent-Driven ArchitecturesAutomated Testing

Soft Skills

Problem-SolvingCollaborationTechnical Specification Writing

Tools & Technologies

DockerLinuxCI/CD PipelinesKubernetesGCPAWSAzureAI Coding Tools

Industry Keywords

AI TechnologiesLLM ApplicationsProduction SystemsPerformance ProfilingSystem ResilienceFault ToleranceObservability

Tech Stack

Tools & technologies

AWSAzureCloudDockerGoogle Cloud PlatformKubernetesLinuxPythonRedis

About the role

Key responsibilities & impact

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

What you’ll need

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 EngineeringYou 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

Comp & perks

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)