Agentic Architect

Caylent’s Anthropic Business Unit is a dedicated AI services practice built in partnership with Anthropic. 

The Agentic Architect is the strategic and architectural lead on AI engagements. Paired with a Forward Deployed Engineer (FDE) who handles hands-on implementation, you own the engagement arc: shaping AI strategy, designing operating models, mapping AI outcomes to business value, and guiding executive stakeholders through adoption. You lead the room — the FDE builds what you architect.

This is a cross-functional role. You engage across lines of business, not just engineering, connecting AI initiatives to the outcomes that matter to each part of the organization. Every engagement is anchored to Anthropic’s platform and focused on real AI adoption outcomes.

Your clients span PE portfolio companies, growth-stage software businesses, and enterprise organizations. You are the strategic face of Caylent from kickoff through handoff.

• Own AI engagements end-to-end as the strategic lead, from discovery through executive handoff, partnered with an FDE who drives implementation
• Run structured discovery across engineering, product, and business stakeholders to map organizational context, AI readiness, and strategic goals across lines of business
• Design AI operating models that define how the organization adopts, governs, and scales AI. Not just within engineering, but across the business
• Define AI outcome maps that tie initiatives to measurable business results: efficiency gains, revenue impact, risk reduction, and time-to-value
• Lead AI maturity assessments and architect near-term and long-term adoption roadmaps aligned to Anthropic’s platform capabilities
• Facilitate executive workshops and enablement sessions for audiences ranging from engineering teams to C-suite leaders
• Deliver executive presentations to CTOs, VPs of Engineering, and line-of-business leaders covering current-state findings, ROI projections, and strategic recommendations
• Direct FDE work on Claude Code tooling, prompt architecture, and workflow automation. You set the technical direction, the FDE executes

• 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 Agentic Architects 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: Agentic Architects 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