Head of Applied AI

Cephas Consultancy Services Private Limited

United States, California, San Francisco

Headcount: 1

Set a cohesive technical direction. Make the product’s architecture understandable and durable. Decide what belongs in the shared product, what should be customer-specific, and where the team should invest or simplify.
Lead the engineering team. Hire excellent people, develop the engineers already here, set a high bar for technical judgment, and create the clarity needed for teams to ship well together.
Build alongside the team. Write production code, tackle difficult technical problems directly, and stay close to the quality and behavior of what we ship. This is not a management-only role.
Be the face of engineering for customers. Join important technical conversations, understand what customers need, explain our decisions clearly, and take ownership when difficult engineering issues need resolution.
Turn direction into delivery. Make trade-offs across reliability, product quality, speed, and customer needs—and ensure architectural decisions show up in a better product, not just in documents.
Set the product and research direction. Identify which technical bets will make Eragon meaningfully better for customers, turn promising research into product priorities, and know when to stop exploring and ship.

You’ve played a pivotal role at a startup or scale-up building and shipping AI agents. You can point to what you personally built, the decisions you made, and how the product changed as a result.
You’ve worked through the hard parts of making agents useful in production: reliability, evaluation, tool use, context, and what happens when an agent gets something wrong.
You’ve shaped the architecture of a product as it grew, making clear decisions about what to build into the platform and what to keep specific to a customer or use case.
You’ve led engineers and helped a team do its best work through hiring, technical direction, honest feedback, and a high bar for shipping.
You still write and ship production code. You want to lead from inside the work, not step away from it.
You can earn a customer’s trust in a difficult technical conversation and take responsibility for resolving the underlying problem.

Experience building and shipping AI agents at a startup or scale-up
Experience with production agent challenges, including reliability, evaluation, tool use, context, and error handling
Experience shaping product architecture as it scales
Experience as an early or founding engineer at a top AI-native company, building core agent systems hands-on, or experience on the AI team at a high-bar company (for example, Databricks/Mosaic, Ramp AI, or Clay AI)
Deep, opinionated views on agent harness architecture, including short- and long-term memory, evaluations and benchmarks, and permissioning in enterprise settings
A hands-on builder’s approach rather than a purely managerial one