Forward Deployed AI Lead

Identify expansion opportunities across lines of business. AI strategy work naturally surfaces adjacent needs in departments beyond the initial engagement

Shape follow-on scope throughout the engagement: by Week 1, you should have a working hypothesis for the next engagement; by the final on-site block, you present it

Build executive relationships that position Caylent as a long-term AI strategy partner, not a one-time vendor

Attend on-site for the final Catalyst block: readout, workshop, and executive presentation

Contribute reusable strategy frameworks, delivery artifacts, and methodology improvements back into the practice at engagement close

Lead AI strategy engagements spanning multiple teams, platforms, and business functions

Collaborate with FDEs to refine the architect-engineer delivery model and improve handoff patterns

8+ years of software development or technical architecture experience across varied stacks, including hyperscaler environments

3+ years in engineering leadership, technical consulting, solutions architecture, or a combination

Demonstrated ability to engage across lines of business. You can talk to a VP of Engineering about CI/CD and a COO about operational efficiency in the same week

Experience designing or advising on AI operating models, technology adoption strategies, or digital transformation programs

Hands-on familiarity with Claude Code or equivalent AI-assisted development tools — you don’t need to configure them daily, but you need to speak the language credibly and direct an FDE’s work

Certified Claude Architect

Experience running structured stakeholder interviews, discovery sessions, and facilitated workshops with both technical and business audiences

Strong executive communication: writing and presenting to CTOs, VPs of Engineering, and line-of-business leaders

Experience delivering professional services engagements, including scope management, deliverable ownership, and client advisory

Working knowledge of CI/CD pipelines, DevOps practices, and modern software development workflows

Experience mapping AI initiatives to business outcomes (revenue, efficiency, risk, time-to-value) with executive-level ROI framing

Familiarity with PE portfolio company environments and board-level communication

Background in solutions architecture, platform engineering, or enterprise technology strategy

Experience with LLM prompt engineering, agentic AI workflow design, or AI product development

Direct experience with Claude Code’s extensibility model

Familiarity with SDLC maturity frameworks or workforce analytics (DORA, DevEx)

Experience designing and facilitating workshops for mixed audiences — engineers, product managers, and business stakeholders

The Forward Deployed AI Lead who do well here are a specific profile:

Sat in the seat. You’ve led engineering teams or technology strategy inside a product company and understand the pressures executives navigate when adopting AI. Clients can tell.

Thinks across the business. You don’t stop at engineering. You see how AI impacts operations, product, and go-to-market — and you can have those conversations with the people who own them.

Knows when to slow down. You surface risks before committing to an approach. When the client’s definition of the work is wrong, you say so.

Closes the next engagement. The final week of a Catalyst is a conversion window. You treat it that way — and the cross-LOB relationships you’ve built give you multiple paths to expand.

Makes the methodology better. Every engagement teaches you something. You feed it back into the practice rather than keeping it in your head.

Earns trust at the executive level. You’re as comfortable presenting an AI operating model to a COO in the morning as reviewing prompt architecture with your FDE that afternoon.

This is a poor fit if you want someone else to handle the sales motion or prefer execution-only roles. You are accountable for both strategic delivery quality and account expansion.

Ground floor of something real: this BU is early, well-resourced, and anchored to Anthropic. You're writing the playbook, not inheriting one

Own the methodology: you are shaping how AI-DLC enablement gets delivered. Every engagement you run improves the one after it

Proximity to the market: your clients are PE portcos, enterprise engineering orgs, and Anthropic-referred customers at the frontier of AI adoption

Revenue upside: Forward Deployed AI Lead who drive account expansion earn on revenue generated, not just delivery margin

AI-native by default: this team uses AI in how it works. Specification-First Engineering is how we move from prototype to production fast

Outcomes, not hours: advancement tied to delivery performance and account growth, not utilization