Applied AI Engineer
Builds production-grade agentic AI features for ContactMonkey's internal-communications SaaS: LLM orchestration, eval harnesses, prompts-as-code, and the AWS/EKS infrastructure behind them. Stack: Ruby on Rails, Vue.js, Amazon Bedrock, Terraform/Terragrunt, Kubernetes, MySQL/PostgreSQL. Hybrid, 3 days/week in downtown Toronto.
- Compensation
- CA$140K–CA$160K / yr
- Location
- Toronto
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 3+ years
- Category
- Ai engineering
- Posted
Skills
Must have
- Bachelor's degree (or higher) in Computer Science, Statistics, Mathematics, or Engineering, or equivalent practical experience
- 3+ years professional software engineering experience, including hands-on AI/ML or LLM-powered features
- Strong fullstack experience in a production SaaS codebase (Ruby on Rails and Vue.js/React preferred)
- Has written and applied infrastructure as code; Terraform preferred (Terragrunt, CloudFormation, or CDK transfers)
- Working knowledge of AWS beyond the console: IAM, VPC/networking, secrets management, containerized deployment and observability
- Has taken LLM work past a prototype at least once (output quality, prompt iteration, notebook-to-production gap)
- Has built evaluation beyond trial and error — measuring accuracy, safety, and latency
- Structures LLM calls for reliability: function calling, structured outputs, failure-mode design (hallucination, latency, cost)
- Can reason about AI feature costs (tokens, context size, retries, caching)
- Familiarity with RAG architectures and vector stores; interest in multi-agent orchestration frameworks
- Defaults to AI tools in own workflow
- Comfortable with agile development
- Able to work collaboratively and independently
- Excellent oral and written communication skills
- Intellectual humility, empathy, and strong listening skills
Nice to have
- Kubernetes familiarity
- Has built something agentic — multi-step LLM reasoning with tool use and feedback (side projects count)
- Has built an eval harness that changed a ship/hold/rollback decision
- Has operated AI in production (cost spikes, provider outages, model deprecations, quality regressions)
- Has worked with Amazon Bedrock or has opinions on model routing and provider abstraction
- Has worked in the email ecosystem or similar high-scale communication platform
- Has built with AI coding agents
- Bias toward simplicity; prototypes fast and changes direction based on data
- Shares learnings with peers (writeups, demos, docs, internal talks)
Full description
Hey there! We're ContactMonkey 👋
Our mission? To power measurable employee engagement worldwide. And we'd love for you to join us!
About the job - Applied AI Engineer
Join the Engineering Team, where you'll help build our agentic future.
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. If you like being close to both the model and the metal, this is that job.
Our 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.
This 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.
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 from.
Your impact
Agentic 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.
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. 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.