{"id":2035474,"url":"https://alion.io/job/contactmonkey-applied-ai-engineer","title":"Applied AI Engineer","company":{"id":674782,"name":"ContactMonkey","domain":"contactmonkey.com","url":"https://alion.io/company/contactmonkey","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Toronto, Canada"],"countries":["CA"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":66000,"max_usd":138000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":14},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"Agile","optional":false},{"name":"AI Agents","optional":false},{"name":"Amazon EKS","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"CloudFormation","optional":false},{"name":"Function Calling","optional":false},{"name":"Hallucination","optional":false},{"name":"IAM","optional":false},{"name":"Kubernetes","optional":false},{"name":"Least Privilege","optional":false},{"name":"LLM","optional":false},{"name":"LLM Evaluation","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"RAG","optional":false},{"name":"React.js","optional":false},{"name":"Ruby on Rails","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Terraform","optional":false},{"name":"Terragrunt","optional":false},{"name":"Vue.js","optional":false},{"name":"ArgoCD","optional":true},{"name":"GitOps","optional":true},{"name":"Gmail","optional":true},{"name":"JavaScript","optional":true},{"name":"Karpenter","optional":true},{"name":"MySQL","optional":true},{"name":"Outlook","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Ruby","optional":true},{"name":"Sidekiq","optional":true}],"status":"live","first_seen_at":"2026-10-07T13:53:48Z","employer_posted_date":"2026-10-13","last_verified_at":"2026-10-14T01:16:56Z","board_verified":true,"closed_at":null,"days_open":6,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":6},"description":"Hey there! We're ContactMonkey\nOur mission? To power measurable employee engagement worldwide. And we'd love for you to join us!\nAbout the job - Applied AI Engineer\nJoin the Engineering Team, where you'll help build our agentic future.\nYou'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.\nThis 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.\nOur stack, concretely: 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; MySQL and PostgreSQL. We're mid-migration to a GitOps deployment model with Argo CD and Karpenter. You'll touch most of this.\nThis is not \"call an LLM and hope it works.\" It's careful system design, honest measurement, and shipping production systems that people rely on every working day.\nThis 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 from.\nYour impact\nAgentic Design & Orchestration: Build agentic workflows and orchestration patterns (dynamic routing, tool-using agents, feedback loops) - contributing to the design and owning the implementation of well-scoped components.\nEvals & 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.\nInfrastructure 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.\nReliability, Cost & Safety: Keep our AI layer trustworthy as it grows. Instrument token spend, latency, and failure rates as first-class metrics. 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.\nPrompts & 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.\nData 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. Think about prompt injection and PII exposure before an auditor does.\nExecution & Experimentation: Take a defined problem and run with it. 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.\nCross-Functional Partnership: Work closely with Product Management to turn product ideas into concrete technical work - both customer-facing features and internal workflow automation.\nGrow With the Team: Participate actively in design discussions and code reviews. Ask questions early, escalate blockers, and share what you learn. Contribute to documentation practices (agents, markdown, Knowledge Bases) as we figure them out together.\nCultural Stewardship: Be a full participant in helping the engineering culture evolve as we grow.\nAbout you\nYou hold a Bachelor's degree (or higher) in Computer Science, Statistics, Mathematics, or Engineering, or equivalent practical experience.\n3+ years of professional software engineering experience, including hands-on work with AI/ML or LLM-powered features.\nStrong 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 first.\nYou've written and applied infrastructure as code yourself. 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.\n Working knowledge of AWS beyond the console: IAM (roles, policies, least privilege), VPC and networking basics, secrets management, and how a containerized workload gets deployed and observed. Kubernetes familiarity is a strong plus.\nYou'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.\"\n 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.\nYou understand how to structure LLM calls for reliability: using function calling and structured outputs to ground results, testing responses systematically, and designing for failure modes (hallucinations, latency, cost).\nYou 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 problem.\nYou 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 landscape.\nYou 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 crutch.\nYou prioritize a \"question first, then answer\" approach, thinking critically about problems before committing to a code path.\nYou bring intellectual humility, empathy, and strong listening skills to the team.\nYou are able to work collaboratively and independently.\nYou are comfortable with agile development.\nYou have excellent oral and written communication skills.\nHow you can stand out\nYou've built something agentic - an LLM reasoning across multiple steps, making decisions from tool outputs, adapting to feedback. Side projects count.\nYou've built an eval harness that changed a decision - where the numbers told you to ship, hold, or roll back, and you listened.\nYou've operated AI in production - you've been on the other side of a cost spike, a provider outage, a model deprecation, or a quality regression that only showed up under real traffic.\n You have a bias toward simplicity - you're pleased when a well-structured baseline beats something more complex, and you know when not to reach for a model at all.\nYou've worked with Amazon Bedrock, or have opinions about model routing and provider abstraction earned the hard way.\nYou've worked within the email ecosystem or a similar high-scale communication platform.\nYou've built with AI coding agents and have a view on what they're good and bad at - we use them, and we're still figuring out the right practices.\nYou prototype fast, iterate quickly, and use data to change direction when the evidence says so.\nYou share what you learn with peers - writeups, demos, docs, internal talks.\nWhat we bring to the table\n 100% employer-paid benefits + a Health Spending Account from day one\n Work from anywhere in the world for up to 4 weeks\n Stock option plan-own a piece of our success\n RRSP Group Savings Plan to plan for your future\n Generous vacation package to recharge and relax\n Personal development budget to fuel your growth\n One personal day + two volunteering days to give back\n Your Birthday off-celebrate on us!\n Five health days per year to stay at your best\n Beautiful downtown Toronto office for hybrid work-fully stocked with all the best snacks\nCompensation & Work Details\nThe salary range for this role is $140,000-$160,000. Compensation is thoughtfully determined based on your experience, skill set, and alignment with our internal compensation framework and internal equity.\nWe're always happy to answer questions about compensation throughout the hiring process.\nThis is a net new position for our AI Engineering Team based out of our downtown Toronto office, at King and Spadina. Our team works in the office 3x per week or as needed to promote collaboration.\nWho We Are\nImagine being part of a team of brilliant minds, all shaping the future of workplace communication. Here at ContactMonkey, we're not just sending out traditional emails with our internal comms software; we're changing the way companies connect and communicate with their people. Brands like IKEA, Roku, KPMG, and countless others are using our solution to transform employee engagement.\nOur all-in-one platform-featuring a drag-and-drop email builder, engagement tools, and analytics-makes it easy for businesses to create, send, and measure their internal email campaigns directly within Outlook or Gmail. This way, internal communications go from being ignored to binge-worthy, sparking more opens, clicks, and conversations.\nWe've been on an explosive growth streak over the past few years, and we're not slowing down anytime soon. Here's a bit of what we're proud of:\nRanked by the Globe & Mail as one of Canada's fastest-growing companies\nRecognized in 2023, 2024 and 2025 Deloitte Technology Fast 50™ awards for revenue growth over the past four years\nRecognized in 2023, 2024 and 2025 Deloitte Technology Fast 500™ as one of the fastest-growing companies in North America\nRaised $55 million Series A financing in 2023\nDiversity is our strength\nAt ContactMonkey, we are building diverse products and we need a diverse team to do that. We strongly encourage applications from everyone regardless of race, religion, colour, national origin, gender, sexual orientation, age, marital status, or disability status.\nWe are committed to creating an accessible experience for all candidates. If you require any accommodations or adjustments during the interview process or beyond, please inform us, and we will work with you to ensure the necessary support is in place. We are continually striving to enhance our accessibility practices and welcome any feedback or suggestions on how we can better serve candidates with accessibility.\nAI Disclosure\nWe use AI to take notes during our interview. Applications and interviews are reviewed by our Talent Acquisition team. 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