Original listing text, shown exactly as published by the company.
What We Are Looking For
Enterprise Lead AI Engineers are senior technical leaders accountable for the technical success of Distyl’s most complex enterprise AI deployments. They may own one large customer engagement or provide technical oversight across multiple smaller accounts, maintaining technical coherence across workstreams, developing technical leaders, and ensuring production AI systems deliver durable business value in complex customer environments.
This role is for leaders who can operate across altitudes: reasoning with customer executives about tradeoffs and delivery risk, setting architecture across multiple teams, developing Forward Deployed AI Engineer Leads and senior engineers, and staying close enough to the work to make sound technical judgments about AI system behavior, code quality, evaluations, integrations, and production reliability.
Key Responsibilities
- Lead the technical success of complex enterprise AI deployments, either across one large customer engagement or a portfolio of smaller accounts, often spanning multiple use cases, production systems, workstreams, and teams
- Set technical direction across engagements, ensuring architecture, system behavior, evaluation strategy, integrations, and operational practices remain coherent as deployments grow
- Develop Forward Deployed AI Engineer Leads, senior ICs, and emerging technical leaders, raising the bar for technical judgment, code quality, customer communication, and production ownership
- Serve as the senior technical partner to customer executives, enterprise architects, and business partners, building trust through clear communication, sound judgment, and reliable delivery
- Maintain technical proximity to live systems by reviewing designs, interrogating system behavior, debugging critical issues, guiding evaluation strategy, and participating in incident reviews and production quality improvements
- Use AI-native engineering practices to increase your own leverage and model effective use of AI tools for design, implementation, debugging, analysis, experimentation, and operational improvement
- Partner with company leadership on staffing, organizational design, leadership development, and customer expansion as engagements grow from pilot to enterprise-scale deployment
What We Require
- 8+ years of software engineering experience, including experience leading technical teams through ambiguous, high-stakes production delivery
- Multi-team technical leadership. You have led technical execution across multiple teams, workstreams, accounts, or senior engineers. You know how to create clarity, set standards, develop leaders, and keep teams aligned without becoming a bottleneck
- Talent development. You raise the capability of the engineers around you. You can coach Tech Leads, senior ICs, and emerging leaders, and you know how to build a talent-dense engineering team over time. Prior people management experience is strongly preferred, but not required if you have demonstrated organizational leadership across multi-team engineering efforts
- Enterprise technical ownership. You take responsibility for whether complex technical deployments deliver durable customer value. You can reason across architecture, delivery risk, team capability, customer constraints, system behavior, and production operations
- Technical proximity and judgment. You can still reason deeply about production software. You understand architecture, debugging, testing, observability, performance, maintainability, security, and code quality. While you mostly operate through your teams, you are willing and able to do selective, high-leverage technical work when it materially improves outcomes
- Customer and executive judgment. You can operate effectively in high-trust, high-visibility customer environments. You communicate clearly with executives, IT leaders, data teams, and operational SMEs about system behavior, delivery tradeoffs, risks, and limitations
- AI-native working style. You use AI tools daily to write and debug code, explore designs, analyze data, automate repetitive work, and improve your own leverage. You are curious about new model capabilities and actively incorporate them into how you lead and build
- Willingness to travel. Travel is typically 20–40%, depending on the project, customer needs, and your role on the engagement
