Distyl AI
AI Engineer
About this role
Distyl AI seeks a Forward Deployed AI Engineer to build and operate production AI systems within customer environments across telecom, healthcare, insurance, and other industries. This hands-on role involves designing end-to-end AI workflows, developing robust Python services, and iterating on live systems to deliver measurable business value with accountability for production outcomes.
What you'll do
- Build and operate AI systems in customer environments, owning system reliability and production performance
- Design compound AI workflows combining models, prompts, agents, tools, and retrieval into coherent systems
- Develop maintainable Python services that integrate AI into customer workflows and existing applications
- Measure system behavior, identify failures, debug issues, and iterate to improve quality and user value
- Build evaluation frameworks and observability patterns to understand and improve AI system performance
- Partner with customers and subject matter experts to understand workflows and adapt systems to evolving needs
What they're looking for
- 2+ years software engineering experience
- Python development and production software systems
- Building AI/LLM applications and composing multi-component systems
- System design, debugging, testing, and performance optimization
- AI-native tools and workflows for code, analysis, and automation
- Customer collaboration and requirements clarification
- Evaluation frameworks and observability design
- Ownership mentality with accountability for outcomes
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Distyl AI
Distyl AI builds Distillery, an AI-native platform that powers enterprise workflows for Fortune 500 companies across industries like telecom, healthcare, and insurance. The company is hiring backend engineers, forward-deployed AI engineers, and evaluation engineers to develop scalable cloud infrastructure, deploy production AI systems, and build evaluation frameworks that drive continuous improvement.
View all jobs at Distyl AILikely interview questions
- Walk us through a production AI system you've built end-to-end. What were the biggest challenges in getting it to work reliably in practice, and how did you debug and iterate when it failed?
- Describe your experience building compound AI workflows. How have you combined elements like prompts, retrieval, agents, or tools together, and how did you evaluate whether the overall system was working?