Agent Deployment Engineer
Decagon is hiring an Agent Deployment Engineer in Toronto, Canada. It pays CAD 150k-CAD 200k a year and Level rates it ; you can apply on Decagon.
Level's read on this job
- On Level's scale, this role is Works on AI ●●●○: the daily work is on AI products, without building the model.
- The posting mentions LLMs, AI agents and customer facing among its tools and skills.
- Level classes it as a mid-level role.
- Decagon has 133 other open roles on Level.
AI in this role
Technical delivery role responsible for end-to-end configuration, building, and deployment of enterprise conversational AI agents.
About Decagon
Decagon is the leading conversational AI platform empowering every brand to deliver concierge customer experiences.
Our technology enables industry-defining enterprises like Avis Budget Group, Block’s Cash App and Square, Chime, Oura Health, and Hunter Douglas to deploy AI agents that power personalized, deeply satisfying interactions across voice, chat, email, SMS, and every other channel.
We’re building a future where customer experiences are being redefined from support tickets and hold music to faster resolutions, richer conversations, and deeper relationships. We’re proud to be backed by world-class investors who share that vision, including a16z, Accel, Bain Capital Ventures, Coatue, and Index Ventures, along with many others.
We’re an in-office company, driven by a shared commitment to excellence and velocity. Our values — Just Get It Done, Invent What Customers Want, Winner’s Mindset, and The Polymath Principle — shape how we work and grow as a team.
About the Team
Over the past few years, development of LLMs has evolved at a rapid pace. It’s not enough for our customers to just “set it and forget it” when it comes to AI software. Truly successful AI Agents require hands-on execution, rigorous iteration, and deep technical delivery to reach enterprise-grade performance.
We’re creating an Agent Deployment Engineering Org: a specialized technical delivery team responsible for end-to-end execution of AI agent builds. Agent Deployment Engineers own the hands-on work required to deliver best-in-class agents—writing and configuring key components, validating integrations, and ensuring agents perform reliably at scale. This team brings greater specialization and focus to build quality. Agent Deployment Engineers are technical and customer-facing: you’ll interface with senior technical stakeholders on the customer side while also going deep on agent configuration and build execution.
About the Role
As an Agent Deployment Engineer, you will be responsible for end-to-end execution of AI agent builds for strategic customers. This is a highly technical delivery role where you will own the implementation work required to launch and scale enterprise-quality agents—partnering with customers and internal teams to define success, translate requirements into build plans, and deliver agents that meaningfully impact the customer’s business.
You’ll work closely with Agent PMs, Agent Success, Engineering, and GTM teams to create tight feedback loops: executing builds, surfacing gaps, and influencing the evolution of Decagon’s agent-building platform based on real customer needs. This role is ideal for someone who thrives in fast-moving environments, enjoys shipping customer-facing technical work, and can balance hands-on execution with structured thinking.
In this role, you will
Own end-to-end architecture and execution of AI agent builds for enterprise customers, from systems design and initial scoping through implementation, evaluation, and production deployment.
Design and engineer the core agentic logic governing agent behavior, applying engineering principles to optimize quality, reliability, and correctness across non-deterministic model outputs.
Engineer and validate layered guardrails and supervisory controls to ensure safe, compliant, and predictable agent performance across real-world scenarios.
Architect, build, and test integrations with customer systems (e.g., data pipelines, CRMs, ticketing systems), including building the tools, APIs, and workflows needed for reliable deployments at scale.
Interface with senior technical stakeholders at customers to define success criteria and system requirements, and drive technical delivery against timelines.
Diagnose, debug, and resolve both probabilistic and technical failures through root-cause analysis of execution traces, error logs, and model behavior.
Design evaluation and regression-testing frameworks to validate agent behavior against ground truth and guard against performance drift.
Translate customer needs into clear internal documentation and run tight feedback loops with Engineering to drive platform improvements.
Partner closely with APMs, Engineering, Design, and Go-To-Market teams to deliver consistent, repeatable, best-in-class agent builds.
Your background looks something like this
Have 5+ years of relevant experience in a technical customer-facing role (e.g., solutions engineering, forward-deployed engineering, technical consulting, implementation engineering, technical product/PM, or similar).
Strong technical foundation: comfortable writing code, working with APIs, and building/validating integrations end-to-end.
Experience delivering production-grade customer solutions that require structured execution, testing/validation, and iteration.
Ability to communicate clearly with senior technical stakeholders, translate requirements into implementation plans, and drive delivery.
Comfort working in fast-moving, ambiguous environments where you shape solutions as much as you implement them.
Even better if you have
Experience building with or around LLMs / AI agents (prompting, evaluation, guardrails, tooling, workflow design, etc.).
Experience with enterprise SaaS integrations (e.g., ticketing systems, CRM, data pipelines) and associated security/compliance considerations.
A Computer Science, Engineering, or Math degree, or equivalent technical experience.
Strong product instinct: ability to write crisp PRDs, define success metrics, and contribute customer insight back into product roadmap.
Benefits
We proudly offer the following benefits for our full-time employees:
Medical, Dental, and Vision benefits for you and your family
Life Insurance and Disability Benefits
Retirement Plan (e.g., 401K, pension)
Parental Leave
Fertility and family building benefits through Carrot
Monthly stipend to support your wellness, lifestyle, and work-life balance
Daily lunches and snacks in the office to keep you at your best
Take what you need vacation policy (subject to local requirements; UK employees receive 25 days of statutory leave)
These benefits are described in more detail in Decagon’s policies, may vary by location, and can change at any time according to applicable compensation and benefits plans.
How we rate this
Agent Deployment Engineer at Decagon rates 70 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.
Works on AI. The daily work is on AI products, without building the model.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- How do you decide when an AI agent can act on its own versus asking for approval first?
- Tell me about a project where customer facing was part of your work. What did you do?
- Tell me about a project where technical delivery was part of your work. What did you do?
- Tell me about a project where system integration was part of your work. What did you do?
- Walk me through how you've used LLMs in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: AI agents, Customer Facing, Technical Delivery, System Integration, and LLMs. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.
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