Join our Engineering team, where you'll help build our agentic future. Our Applied AI Engineers don't just call an LLM and hope it works. We design careful systems, measure honestly and ship production AI that people rely on every working day.
You'll report to our Senior AI Engineering Manager and team up with senior engineers, Product and our Chief Product & Technology Officer to help us design, prototype, and ship AI-powered capabilities quickly. Expect smart people, big goals and plenty of room to make your mark.
This is an AI engineering role with real infrastructure ownership. You won't be handed a platform; you'll help build the one our AI features run on. If you like being close to both the model and the metal, this is that job.
Our stack: Ruby on Rails and Vue.js in a production SaaS codebase, Amazon Bedrock for model inference, AWS on EKS provisioned with Terraform and Terragrunt across regions, Sidekiq for background work, and MySQL and PostgreSQL. We're mid-migration to a GitOps deployment model with Argo CD and Karpenter, and you'll touch most of it.
What you'll own 🚀
- Agentic workflows: Build agentic workflows and orchestration patterns, like dynamic routing, tool-using agents and feedback loops. You'll help design them and own the implementation of well-scoped components.
- Evals and measurement: Own the eval harness for the features you ship, including datasets, automated grading and offline regression suites that show whether a change actually made things better. Turn production failures into evals that catch that class of failure next time, and raise the bar for evaluation across the team.
- Infrastructure for AI: Build the infrastructure your features depend on, as code, including the deployment path for AI workloads on EKS, alongside our SRE and platform engineers.
- Reliability, cost and safety: Track token spend, latency and failure rates as first-class metrics. Design for hallucinations, timeouts, rate limits and cost blowouts so the system degrades gracefully instead of falling over, and respond when things behave unexpectedly in production.
- Prompts and model config as code: Treat prompts as versioned, reviewable, rollback-able artifacts. Own how a prompt or model change moves safely to production, and how we back it out if it makes things worse.
- Data residency and trust: Make sure AI features respect our regional deployments: customer data stays in-region, model invocations are logged, and what we send to a model is what we intended. Think about prompt injection and PII exposure before an auditor does.
- Experimentation: Take a defined problem and run with it. Contribute to our quarterly AI roadmap, benchmark model value and iterate on prompts, retrieval and model configurations based on the data.
- Partnership and culture: Work closely with Product to turn ideas into concrete technical work, from customer-facing features to internal automation. Join design discussions and code reviews, share what you learn and help our engineering culture evolve as we grow.
What you won't own
- The platform on your own: You'll build AI infrastructure with our SRE and platform engineers, not run the whole cloud platform solo.
- Training foundation models: We use Amazon Bedrock for inference. This is an applied role focused on building with models, not research or pre-training.
- The product roadmap: Product Management sets priorities. You'll shape them with technical insight and turn them into working software.
What you bring to the table 💪
- A Bachelor's degree (or higher) in Computer Science, Statistics, Mathematics or Engineering, or equivalent practical experience
- 3+ years of professional software engineering experience, including hands-on work with AI/ML or LLM-powered features
- Strong fullstack experience, ideally Ruby on Rails (or another backend framework) and Vue.js or React. You're comfortable in a production SaaS codebase and can find your way around unfamiliar systems.
- Hands-on infrastructure as code experience, with Terraform preferred. You've authored modules, read a plan carefully before applying it and recovered from state that didn't match reality. Terragrunt, CloudFormation or CDK experience transfers.
- Working knowledge of AWS beyond the console, including IAM and least privilege, VPC and networking basics, secrets management, and how containerized workloads get deployed and observed. Kubernetes familiarity is a strong plus.
- You've taken LLM work past a prototype at least once, iterating on prompts against real usage and closing the gap between "works in the notebook" and "works for customers"
- You've built evaluation beyond trial and error. You can measure accuracy, safety and latency, and you know when a benchmark win is real and when it's noise.
- You know how to structure LLM calls for reliability, using function calling and structured outputs, testing responses systematically and designing for failure modes
- You can reason about what an AI feature costs to run (tokens, context size, retries, caching) and treat cost as an engineering constraint
- Familiarity with RAG architectures and vector stores, and an interest in multi-agent orchestration frameworks
- You default to AI in your own workflow and use it to move faster, not as a crutch
- A "question first, then answer" mindset, plus intellectual humility, empathy and strong listening skills
- Excellent written and verbal communication, and comfort working both independently and collaboratively in an agile team
Bonus points if… ⭐
- You've built something agentic, like an LLM reasoning across multiple steps and acting on tool outputs (side projects count!)
- You've built an eval harness that changed a decision to ship, hold or roll back
- You've operated AI in production through a cost spike, provider outage, model deprecation or quality regression
- You have a bias toward simplicity and know when not to reach for a model at all
- You've worked with Amazon Bedrock, or have hard-earned opinions about model routing and provider abstraction
- You've worked in the email ecosystem or on a similar high-scale communication platform
- You've built with AI coding agents and have a view on what they're good and bad at
- You prototype fast, let data change your direction and share what you learn through writeups, demos or internal talks
What's in it for you 🎁
🏥 100% employer-paid benefits + a Health Spending Account from day one
🌎 Work from anywhere in the world for up to 20 days a year
💰 Stock options, so you own a piece of what we're building
💲 RRSP Group Savings Plan
🏝 A generous vacation package to fully unplug
📚 A personal development budget to keep levelling up
🧖 One personal day + two volunteering days to give back
🎂 Your birthday off
🍎 Five health days a year
💼 A beautiful downtown Toronto office, stocked with all the best snacks
The details 💵
The salary range for this role is $140,000–$160,000. Where you land depends on your experience, skills and our internal compensation framework, so pay stays fair across the team. Got questions about pay? Ask anytime. We're an open book throughout the process.
This is a net new role on our AI Engineering team, based in our downtown Toronto office at King and Spadina. We're together in the office 3 days a week, because the best ideas happen in person (and so do the snack runs).
Who we are 🐵
We're a team of curious, driven people changing how companies talk to their employees. Brands like Pepsi, Adidas, Panasonic, Dyson and many more use ContactMonkey to make internal comms go from ignored to engaged.
Our all-in-one platform lets teams build, send and measure internal emails right from Outlook or Gmail, with a drag-and-drop email builder, engagement tools and analytics that show what's actually landing. The result? More opens, more clicks and more conversations.
We've been growing fast, and we're just getting started:
- 🏆 Ranked by the Globe and Mail as one of Canada's fastest-growing companies
- 📈 Named to the Deloitte Technology Fast 50™ in 2023, 2024 and 2025
- 🚀 Named to the Deloitte Technology Fast 500™ in 2023, 2024 and 2025 as one of North America's fastest-growing companies
- 💸 Raised a $55M Series A in 2023
Everyone belongs here 💚
We build products for diverse workplaces, and the best way to do that is with a diverse team. We strongly encourage applications from everyone, regardless of race, religion, colour, national origin, gender, sexual orientation, age, marital status or disability status.
We want every candidate to have a great, accessible experience. If you need any accommodations or adjustments during the interview process or beyond, just let us know and we'll make sure the right support is in place. We're always working to improve, so we welcome your feedback, too.
A quick note on AI 🤖
We use AI to take notes during interviews. All applications and interviews are reviewed by real humans on our Talent Acquisition team. Our applicant tracking system uses AI to help with workflows and keep the hiring process running smoothly.
