Decagon — Customer Engineer, Agent Builder (TO)

Type: Full-time | On-site | Toronto, Canada Compensation: $150,000–$200,000 + Competitive Equity Hiring count: 2 Visa sponsorship: H-1B (per Contrario structured field — see note; role is Toronto-based) Reports to: TBD

Decagon is the leading conversational AI platform helping brands deliver concierge-level customer experiences. Enterprises including Avis Budget Group, Cash App and Square (Block), Chime, Oura, and Hunter Douglas run Decagon's AI agents across voice, chat, email, SMS, and other channels. Backed by a16z, Accel, Bain Capital Ventures, Coatue, and Index Ventures.

The Agent Builder org was stood up in Q1 and currently has 7–8 people, working one-to-one with some of Decagon's largest strategic customers (Hertz, Delta, and Amex among them). As the team scales, agent builders will support more customers concurrently. Decagon is an in-office company built around velocity and hands-on execution.

Founded: 2023 | Team size: 201–500 | Stage: Series D+ Industry: Consumer Tech / Conversational AI Website: decagon.ai Office: Toronto, Canada

• Category leader with elite backing: Leading conversational AI platform, live at scale with marquee enterprises (Avis, Cash App, Chime, Oura), backed by a16z, Accel, BCV, Coatue, and Index.
• High-ownership, end-to-end seat: Own agent builds from scoping through launch and iteration; write and configure core components and interface directly with senior technical stakeholders on the customer side.
• Early on a scaling org: Agent Builder team is ~7–8 people, stood up in Q1, working one-to-one with Decagon's largest strategic accounts — early enough to shape how the function scales.

• An intake video is on the Contrario page but was not transcribed in the pasted HTML, so no written intake summary is available yet. Share the transcript or notes to populate this section.

Own end-to-end execution of AI agent builds for enterprise customers — from scoping through launch and iteration. A highly technical delivery seat: write and configure core components, validate integrations, and interface directly with senior technical stakeholders on the customer side. The build/customer split is dynamic, generally landing near 50/50 to 60/40 technical-to-customer-facing.

• →End-to-end execution of AI agent builds for enterprise customers, from scoping through launch and iteration
• →Write and maintain key agent-building artifacts; configure agent behavior for quality, reliability, and business outcomes
• →Configure and validate guardrails for safe, compliant, predictable agent performance
• →Set up, test, and validate customer integrations (ticketing systems and comparable), building any tools or workflows needed
• →Interface directly with senior technical stakeholders to define success criteria and drive delivery against timelines
• →Partner closely with Agent PMs, Engineering, Design, and GTM to deliver consistent, repeatable agent builds

Tech stack: Python (hard requirement); REST APIs and enterprise integrations; LLM / AI-agent tooling (prompting, evaluation, guardrails, workflow design)

• 5–7 years of experience in a technical customer-facing role (solutions engineering, forward-deployed engineering, technical consulting, implementation engineering, technical PM, or comparable)
• Strong Python proficiency (hard requirement)
• API integration experience end to end — comfortable writing code, working with APIs, and building or validating integrations
• Experience delivering production-grade customer solutions requiring structured execution, testing, and iteration
• Clear communicator who can translate requirements into implementation plans and drive delivery with senior technical stakeholders

Nice-to-have: experience building with or around LLMs and AI agents (prompting, evaluation, guardrails, tooling, workflow design); enterprise SaaS integrations (ticketing, CRM, data pipelines) with associated security/compliance exposure; Computer Science, Engineering, or Math degree or equivalent; strong product instinct (crisp requirements docs, success metrics).

• Software engineering background with a genuine, demonstrated pull toward customer-facing work
• Started in software engineering or forward-deployed engineering, then pivoted toward customer-facing technical delivery
• Strong quantitative or Computer Science degree from a well-regarded program
• Comfortable owning both the technical build and the customer relationship without needing the two responsibilities split apart
• Post-sales orientation and interest, since this role sits exclusively on the post-sales side

• Background as an IT manager or in IT operations without transferable hands-on technical build experience
• Currently at Cognition or Sierra (do not contact due to existing relationships)
• Currently at C3 AI or Amazon (historically weak fit in this process)
• Interested only in pre-sales work; this role's lane is exclusively post-sales
• Career built entirely in a single large enterprise without independent technical delivery signal

• Salary — $150,000–$200,000
• Equity — Competitive
• On-site policy — In-office, five days a week
• Visa sponsorship — H-1B per Contrario field (role is Toronto-based — confirm)
• Employment type — Full-time
• Location — Toronto, Canada

Contrario "Required Candidate Q&A" (collected via the Contrario submission form):

1. Phone number
2. LinkedIn
3. Where did you go for college?
4. Are you legally authorized to work in Canada?
5. Will you now or in the future require visa sponsorship to work in Canada?
6. Are you excited to work in-office five days a week?
7. How did you hear about Decagon?

Stage 1 — Recruiter / HM Screen Stage 2 — Initial Screen Stage 3 — Take Home Stage 4 — Onsite Stage 5 — Debrief Stage 6 — Reference Check Stage 7 — Closing Stage 8 — Offer Extended Stage 9 — Candidate Hired — Candidate accepts and starts.