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 - AI Architect 

ContactMonkey's platform already runs AI in production - Claude-powered template editing, a standalone ConfidenceCheck service that evaluates outgoing emails for editorial quality and link safety, Braintrust-backed evaluations, and per-organization feature gating. The next wave is bigger: grounded assistants, retrieval over customer brand and campaign history, richer agentic flows inside the composer, and data products that make sending decisions smarter.

We're forming a dedicated AI pod to own that trajectory. With the team you will be setting the technical direction for the pod, make the foundational infrastructure bets (vector store, evals, data warehouse, agent orchestration), and ship the first production systems yourself. This is an architect role in the sense that you own the shape of the thing - it is not a role where you stop writing code.

Your impact

• AI platform direction: Model selection and routing, prompt and context architecture, evaluation strategy, cost and latency budgets, safety and PII handling. Decide where we go deeper with frontier models, where we fine-tune smaller ones, and where classical ML beats an LLM.
• Retrieval and grounding infrastructure: You'll pick it, design the ingestion path (brand kits, past campaigns, template library, per-org knowledge), own the chunking/embedding/refresh strategy, and set the quality bar via evals.
• Agent and tool-use patterns: Multi-turn tool-calling loops, deterministic fallbacks, observability, recoverable failure modes across Sidekiq and SQS, human-in-the-loop checkpoints where they matter.
• Data engineering foundations: You'll decide whether we need a warehouse (BigQuery / Snowflake / DuckDB-on-S3/ RedShift), design the pipelines that feed both product analytics and model training/eval datasets, and make sure the lineage is clean enough that we trust what we ship. Build a foundation for the next level data analytics across the company.
• Cloud and deployment architecture: We run on AWS (ECS, S3, SQS etc). You'll make the calls on how AI services are deployed - when a new microservice like ConfidenceCheck is the right pattern vs. embedding in the Rails monolith, how we handle GPU/accelerated inference if we ever need it, how we isolate model spend per tenant.

What we expect from you technically

You should be credible across all four of AI, Cloud, Data, and Fullstack - not a specialist in one with passing knowledge of the others. 

Concretely:

AI / ML

• Built and operated LLM-backed features in production against real users.
• Deep familiarity with at least one major provider SDK (Anthropic, OpenAI) and comfort switching between them. Understand prompt caching, structured outputs, tool use, and the tradeoffs between agent loops and pipeline-style orchestration.
• Evals are a reflex, not an afterthought. You've run a real eval harness (Braintrust, promptfoo, in-house) against golden datasets and used it to block regressions.
• Working knowledge of retrieval systems: embeddings, hybrid search, reranking, and where each breaks. Opinions on pgvector vs. dedicated vector DBs and when either is wrong.
• Bonus: fine-tuning or distillation experience, classical ML for ranking/classification, prompt-injection and jailbreak threat modelling.

Cloud

• AWS fluency at the architecture level - VPCs, IAM boundaries, secrets management, queue-based decoupling, cost control. Equally comfortable if we need to run a workload on GCP for a specific reason.
• Container-native deployment (ECS, EKS, or equivalent). IaC experience (Terraform or CDK) - even if we aren't using it yet, you should expect to bring it in.
• Have operated production systems under real load. You've been paged, and you've written the postmortem.

Data Engineering

• Can design a data pipeline end-to-end: ingestion, transformation, storage, serving. Batch and streaming. Have built this in at least one real organization.
• Comfortable with SQL and non SQL at a level that lets you reason about query plans, not just write queries. You've tuned a slow query by looking at the plan, not by guessing.
• Opinion on when a warehouse earns its keep vs. when read replicas are enough. We're at that decision point.
• Bonus: dbt, Airflow / Dagster / Prefect, CDC patterns, multi-tenant data partitioning at scale.

Fullstack

• Our backend is Ruby 3.4 on Rails 7.2 with Sidekiq Pro and SQS; our frontends are Vue 3 (main app), Nuxt 4 (recipient-facing content), and a Fastify/TypeScript AI microservice. You don't need all of this on day one, but you should be a polyglot who ramps quickly.
• Write real TypeScript. The AI-adjacent services are TS-first and that is likely to grow.
• Understand API design past the happy path - versioning, auth modes (we run session auth for the main API and bearer tokens for integrations), rate limiting, idempotency.

What we bring to the table 

🏥 100% employer-paid benefits + a Health Spending Account from day one

🌎 Work from anywhere in the world for up to 4 weeks

💰 Stock option plan—own a piece of our success

💲 RRSP Group Savings Plan to plan for your future

🏝 Generous vacation package to recharge and relax

📚 Personal development budget to fuel your growth

🧖 One personal day + two volunteering days to give back

🎂 Your Birthday off—celebrate on us!

🍎 Five health days per year to stay at your best

💼 Beautiful downtown Toronto office for hybrid work—fully stocked with all the best snacks

Compensation & Work Details

Compensation is thoughtfully determined based on your experience, skill set, and alignment with our internal compensation framework and internal equity.

We’re always happy to answer questions about compensation throughout the hiring process.

This is a net new position based out of our downtown Toronto office, at King and Spadina. Our engineering team works primarily remote but we are looking for someone who is willing to come into the office occasionally on an as needed basis.

Who We Are 

Imagine 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, KMPG, and countless others are using our solution to transform employee engagement.

Our 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.

We’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:

• Ranked by the Globe & Mail as one of Canada’s fastest-growing companies
• Recognized in 2023, 2024 and 2025 Deloitte Technology Fast 50™ awards for revenue growth over the past four years
• Recognized in 2023, 2024 and 2025 Deloitte Technology Fast 500™ as one of the fastest-growing companies in North America
• Raised $55 million Series A financing in 2023

Diversity is our strength

At 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. 

We 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.

AI Disclosure: 

We use AI to take notes during our interview. Applications and interviews are reviewed by our Talent Acquisition team. Our applicant tracking system utilizes AI for workflows and hiring process efficiencies.

Job Summary: 

We are seeking a highly skilled and self-motivated Senior Embedded DevOps Engineer to support our engineering  teams. This role will focus on driving changes and ensuring adherence to company-established standards for data infrastructure and CI/CD pipelines. The ideal candidate will have strong experience working with AWS and/or GCP,  cloud-based data streaming and processing services, containerized application deployments, infrastructure automation, and Site Reliability Engineering (SRE) best practices for performance and cost optimization.

What You'll do:

• Drive initiatives to implement and enforce best practices for data streaming, processing, analytics and monitoring infrastructure.
• Deploy and manage services on Kubernetes-based platforms such as Amazon EKS and Google Kubernetes Engine (GKE).
• Provision and manage cloud infrastructure using Terraform, ensuring best practices in security, scalability, and cost-efficiency.
• Maintain and optimize CI/CD pipelines using Jenkins, ArgoCD, and GitHub Enterprise Actions to support automated deployments and testing.
• Work with cloud-native data services such as AWS Kinesis, AWS Glue, Google Dataflow, and Google Pub/Sub, BigQuery, BigTable
• Familiarity with  workflow orchestration services such as Apache Airflow and Google Cloud Composer.
• Develop and maintain automation scripts and tooling using Python to support DevOps processes.
• Monitor system performance, troubleshoot issues, and implement proactive solutions to enhance reliability and efficiency.
• Implement SRE practices to improve service reliability, scalability, and cost-effectiveness.
• Analyze and optimize cloud costs, identifying areas for improvement and implementing cost-saving strategies.
• Ensure compliance with security policies and best practices in cloud environments.
• Drive adoption of company standards and influence data teams to align with best DevOps and SRE practices.
• Collaborate with cross-functional teams to improve development workflows and infrastructure.

What you bring:

• 7+ years of experience in a DevOps, Site Reliability Engineering, or Cloud Infrastructure role.
• Strong experience with AWS and GCP data services, including Kinesis, Glue, Pub/Sub, and Dataflow.
• Proficiency in deploying and managing workloads on Kubernetes (EKS/GKE) in production environments.
• Hands-on experience with Infrastructure-as-Code (IaC) using Terraform.
• Expertise in CI/CD pipeline management using Jenkins, ArgoCD, and GitHub Enterprise Actions.
• Programming skills in Python for automation and scripting.
• Experience with observability and monitoring tools (e.g., Prometheus, Grafana, Datadog, or CloudWatch).
• Strong understanding of SRE principles, including performance monitoring, incident response, and reliability engineering.
• Experience with cost optimization strategies for cloud infrastructure.
• Self-motivated and driven, with a strong ability to influence and drive changes across multiple teams.
• Ability to work collaboratively in an agile environment and support multiple teams. 

Preffered Qualifications:

• Experience with data lake architectures and big data processing frameworks (e.g., Apache Spark, Flink, Snowflake, BigQuery).
• Familiarity with event-driven architectures and message queues (e.g., Kafka, RabbitMQ).
• Experience with workflow orchestration tools such as Apache Airflow and Google Cloud Composer.
• Knowledge of service mesh technologies like Istio.
• Experience with GitOps workflows and Kubernetes-native tooling.

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