AI Engineer

Join Distyl as an AI Engineer, where you'll build and operate production AI systems that deliver business value. You'll work directly with customers, subject matter experts, and other engineers to design, implement, deploy, and iterate on end-to-end AI systems. This role requires strong engineering fundamentals, a willingness to travel, and experience building AI systems. You'll have the opportunity to make a significant impact in a collaborative and supportive work environment.

• Construire et exploiter des systèmes d'IA déployés dans des environnements clients, en prenant la responsabilité du comportement, de la fiabilité et de l'utilité du système en production.
• Concevoir et mettre en œuvre des flux de travail d'IA complexes qui combinent des modèles, des invites, des agents, des outils, des évaluations, des boucles de rétroaction et des exécutions en systèmes de production cohérents.
• Développer des services Python propres et maintenables et la logique d'application qui intègrent les capacités d'IA dans les flux de travail des clients, les plateformes de données, les API et les applications existantes.
• 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
• 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
• Willingness to travel. Travel is typically 10–30%, depending on the project, customer needs, and your role on the engagement
• 2+ years of software engineering experience
• 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
• 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
• 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
• 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