AI System Lead Engineer

2 days ago


San Francisco, California, United States Distyl AI Full time
About Distyl AI
Distyl AI develops innovative AI solutions that power core operational workflows for the Fortune 500. In partnership with OpenAI, we bring deep expertise in enterprise AI and technical investments to support the rapid development of production-grade AI systems.

Led by proven leaders from top companies like Palantir and Apple, our team utilizes strategic partnerships, software accelerators, and enterprise AI expertise to deliver customized AI products that meet customers' needs within a quarter.

We are VC backed by Coatue, Dell Technologies Capital, leaders like Nat Friedman (Former CEO of Github) and Brad Gerstner (Founder and CEO of Altimeter), and board members of over a dozen F500s.

What We Are Looking For
We are seeking an experienced Lead AI Engineer who can lead the development of production LLM systems for our F500 clients' largest workflows. This requires exceptional engineers who can push the boundaries of what is possible with LLM solutions.

As a Lead AI Engineer at Distyl, you will operate as the CTO of an engagement with a F500 customer, taking ownership of the project's technical direction. You will have a vision for the future and won't be afraid to pitch bold ideas to F500 Enterprise Architects. From there, you will lead a team of engineers to fundamentally transform how your customer operates. Always looking to grow, you will be driven by a passion for mastering your craft and excited to take on leadership roles that shape the future of AI.

Required Skills and Qualifications
To succeed in this role, you should possess:
  • Exceptional software engineering skills, with experience in designing and writing stable, maintainable, and secure systems.
  • Strong knowledge of AI systems, including experience with RAG systems, prompt engineering, and precision/recall metrics.
  • Excellent communication and organizational abilities, with experience leading teams and working with customer technical teams.
  • A deep understanding of enterprise cloud architectures and ability to deploy systems into complex hybrid environments.
  • A bias towards showing results rather than discussing theoretical concepts.

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