You’ll work alongside senior engineers, Product, and our Chief Product & Technology Officer (CPTO) to design, prototype, and ship AI-powered capabilities quickly This role is hands-on and iterative, focused on building production-grade agentic workflows that improve how internal communications are curated, designed, delivered, measured, and orchestrated 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 Agentic Design & Orchestration: Build agentic workflows and orchestration patterns (energetic routing, tool-using agents, feedback loops)
- contributing to the design and owning the implementation of well-scoped components Evals & Measurement: Own the eval harness for the features you ship. Build the datasets, the automated grading, and the offline regression suites that tell us whether a prompt or model change actually made things better. Raise the bar for evaluation across the team
- judge design, offline and online metrics, and the judgment to know when a number is real. Turn production failures into evals that catch that class of failure next time Infrastructure for AI: Own work with SRE and build the infrastructure your features depend on, as code. Build the deployment path for AI workloads on EKS alongside our SRE and platform engineers Reliability, Cost & Safety: Keep our AI layer trustworthy as it grows. Design for the failure modes that matter
- hallucination, timeout, rate limit, cost blowout
- and make sure the system degrades gracefully instead of falling over. Respond when things behave unexpectedly in production Prompts & Model Configuration as Code: Treat prompts as versioned, reviewable, rollback-able artifacts rather than strings someone edited in a console.



Own how we move a prompt or model change from idea to production safely, and how we back it out when it turns out worse Data Residency & Trust: We run regional deployments because our customers require it. Help make sure AI features respect those boundaries
- that customer data stays in-region, model invocations are logged, and what we send to a model is what we intended to send. Contribute to our quarterly AI roadmap, run experiments to benchmark model value, and iterate systematically on prompts, retrieval, and model configurations based on what the data tells you Cross-Functional Partnership: Work closely with Product Management to turn product ideas into concrete technical work
- both customer-facing features and internal workflow automation Be a full participant in helping the engineering culture evolve as we grow Personal Development Budget Bi-weekly Lunch & Learn Monthly Team-building Activities Health & Dental Coverage Bonus Opportunities Extended Vacation Days (34 days of vacation annually, with summer Fridays, and more) This is a great fit if you’ve shipped LLM features in production, you’re comfortable when the answer involves a Terraform plan rather than a prompt tweak, and you’re ready to go deeper
- with senior engineers around you to learn fromYou are able to work collaboratively and independentlyYou have excellent oral and written communication skillsYou’ve written and applied infrastructure as code yourself. Terragrunt, CloudFormation,



or CDK experience transfersYou default to AI in your own workflow. You’ve already integrated modern AI tools into how you work, and you use them to move faster rather than as a crutchYou bring intellectual humility, empathy, and strong listening skills to the teamYou have familiarity with RAG architectures and vector stores, and an interest in multi-agent orchestration frameworks
- you don’t need to have run them at scale, but you should know the landscapeYou can reason about what an AI feature costs to run
- tokens, context size, retries, caching
- and you treat that as an engineering constraint rather than someone else’s problemYou prioritize a “question first, then answer” approach, thinking critically about problems before committing to a code pathYou hold a Bachelor’s degree (or higher) in Computer Science, Statistics, Mathematics, or Engineering, or equivalent practical experienceYou are comfortable with agile developmentYou’ve taken LLM work past a prototype at least once
- you’ve thought about output quality, iterated on prompts against real usage, and dealt with 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 noiseStrong fullstack experience
- Ruby on Rails (or any other backend language/framework) and Vue.js/React (or any other framework) preferred. You’re comfortable in a production SaaS codebase and can navigate unfamiliar systems without needing everything explained first3+ years of professional software engineering experience, including hands-on work with AI/ML or LLM-powered featu

📌 Applied Artificial Intelligence Engineer (Canada)
🏢 ContactMonkey
📍 Canada