AI Engineer (French Speaker)

• AI Engineers build and operate production AI systems that deliver business value inside customer environments
• This role is for engineers who thrive in ambiguous problem spaces, take ownership of outcomes, and want to work directly on AI systems that must perform reliably under enterprise constraints
• AI Engineers are hands-on builders
• They design, implement, deploy, and iterate on end-to-end AI systems in close partnership with customers, subject matter experts, and other Distyl engineers
• They translate messy operational needs into concrete system behavior, build the software and AI workflows required to support that behavior, and continuously improve systems through evaluation, feedback, integration, and production iteration
• This is not a demo-building role
• AI Engineers are expected to make AI systems work in practice: with users, data, constraints, and accountability for production outcomes
• Build and operate AI systems deployed in customer environments, taking ownership of system behavior, reliability, and usefulness in production
• Design and implement compound AI workflows that combine models, prompts, agents, tools, retrieval, evaluation, feedback loops, and execution into coherent production systems aligned with user and SME needs
• Develop clean, maintainable Python services and application logic that integrate AI capabilities into customer workflows, data platforms, APIs, and existing applications
• Operate on live systems by measuring behavior, identifying failure modes, debugging issues, and iterating rapidly to improve quality, reliability, and user value
• Build evaluation frameworks, test cases, feedback mechanisms, and observability patterns that help teams understand and improve AI system performance over time
• Work directly with customer stakeholders and subject matter experts to understand workflows, clarify requirements, reason about tradeoffs, and adapt systems as needs evolve
• Use AI-native engineering tools to accelerate implementation, debugging, experimentation, data analysis, and system improvement
• Collaborate with other AI Engineers, AI Strategists, and other Distillers to make pragmatic system design decisions that balance speed, robustness, maintainability, and customer impact
• Take accountability for the production outcomes of the components, workflows, and systems you build

Benefits

• Equity options and healthcare
• Collaborative and supportive work environment
• Strong focus on personal and professional growth- AI-native working style. You use AI tools daily to write and debug code, explore designs, analyze data, and automate repetitive work. You are curious about new model capabilities and techniques, and actively incorporate them into how you build and iterate on systems
• Fluent in French (Written and Spoken) with the ability to lead technical discussions and collaborate directly with French-speaking clients
• Comfort in customer environments. You are able to work directly with customer teams, ask good questions, and adapt quickly to new domains. You communicate clearly about system behavior, limitations, and tradeoffs, and can operate effectively in high-trust, high-visibility situations
• Willingness to travel. Travel is typically 10–30%, depending on the project, customer needs, and your role on the engagement
• 5+ years of software engineering experience
• Strong engineering fundamentals. You write clean, maintainable Python and are comfortable building production software systems. You understand core engineering concepts like versioning, debugging, testing, performance, code review, and production readiness
• Pragmatic delivery mindset. You can navigate ambiguity, make progress with incomplete information, and balance speed with robustness when building systems that need to work for production users
• Experience building AI systems. You have built applications powered by LLMs or other AI models and are comfortable composing multiple components — prompts, agents, tools, retrieval, evaluators, workflows, and integrations — into end-to-end systems. You reason about system behavior holistically rather than treating models as black boxes
• Ownership mentality for AI systems. You take responsibility for whether the systems you build deliver their intended value in production. You are comfortable making technical decisions, learning from system behavior, and owning the results of your work