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Senior AI Developer, Agentic Systems

AI Summary
Workleap is seeking a Senior AI Developer to build production-grade agentic systems that support product operations across its product lines. The role focuses on multi-agent orchestration, context and memory design, tool integrations, evaluation, observability, security, and cost management. The developer will collaborate closely with Product Managers to deliver iterative workflow agents with human approval gates. This is an early-stage, high-impact engineering position requiring strong technical judgment and the ability to establish reusable architecture and development practices. The position is remote within Canada and includes optional access to the Montreal office.
Remote from
Canada
Salary
CAD 140k–170k / yr
Employment
Full Time
Experience
Senior
Published
Apply before
11 Dec 2026
Listing views
24
Application actions
2
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Opportunity details

About this role.

Company Description

Workleap is a Montreal-based tech company, founded in 2006. We’re builders at heart, we make simple products that actually matter to the people who use them. We have two product lines: Workleap Agent, our newest solution built to make every manager more effective, and ShareGate, the world’s leading solution for Microsoft 365 migration and governance. More than 15,000 companies worldwide trust us to do exactly that. We’re intentional about who joins us. If you’re the kind of person who gets excited by a hard problem and wants to help shape what comes next, there’s a place for you here.

Your role

At Workleap, our engineering teams already ships more than twice as fast with AI. Now we’re expanding beyond code to everything around it: briefs, specs, design, launch, and measurement. The idea is simple: agents do the work, and people make the calls. We’ve are rethinking how we build products and are exploring the technical possibilities, this is just the beginning. As a Senior AI Developer, you’ll join us to shape and build this solution ensuring that we have solid technical foundation.

Your impact:

  • Ship the first production agents with our Product Managers, starting with brief creation and the ProductManager-to-design handoff, then extend to the rest of the product lifecycle.
  • Shape the shared architecture and standards (orchestration, skills, memory, evals, guardrails) so that both our product lines are built on the same foundation.
  • Own the production quality of these agents: observability, testing, security, and cost built in from the start, with human approval at every gate.
  • Co-design with Product Managers: understand how they work, reuse what they’ve already automated, and ship small iterations they test and adopt.
  • Measure what works: time saved, time from customer signal to shipped decision, and cost per agent and per feature, then cut what doesn’t add value.
  • Grow others: share your agent development practices so the team can build, run, and improve agents on their own.

Your team

Over the next 6 to 12 months, you’ll work on questions like: how does an agent turn an idea into a brief that a Product Manager approves without rewriting it? How do specialized agents pass context without losing it or blowing up costs? How do we decide an agent is reliable enough to lighten a gate? The vision is set, and a proof of concept in Slack is underway: you’re joining early enough to shape the architecture and turn this start into a production standard.

You’ll join the Product Ops team, a small team serving Product Manager across two product lines that already owns tracking, product data quality, and analytics. You’ll work daily with this team, our AI-Software Development Life Cycle team, which already automates software delivery, and with our Product Managers working on both product lines. You’ll also partner with the Senior AI Developer on our Data team to share patterns and tooling. We use AI every day, decide fast, and ship in iterations.

Our technology ecosystem includes Claude (Claude Code, skills, Managed Agents, Design), MCP, Slack, Jira, Confluence, GitHub, Mixpanel, Snowflake, Azure, etc.

What you’ll bring

  • Strong production engineering across the agent development lifecycle;
  • Hands-on experience designing and shipping multi-agent systems to production: orchestration, tools, context, and memory, integrated with tools like Slack, Atlassian, and MCP;
  • Experience instrumenting and evaluating non-deterministic systems: evals, observability, regression testing, and cost tracking;
  • Sound architectural judgment across reliability, security, and cost, including which model fits which job, and the confidence to form and defend a technical opinion;
  • Experience building components or tooling that other people adopt;
  • Real product sense: you understand how a Product Manager works and you build with users, not for them;
  • A taste for building as a team and helping people change how they work, by sharing your practices;
  • A builder mindset: you own what you ship, you’re comfortable with the ambiguity of an early-stage project, and you use AI-assisted development tools daily and critically;
  • Bonus: product management experience, or experience on a B2B SaaS, HR, or IT product.

Most of this sounds like you? Apply.

The internal title for this role is Senior AI Developer – Product Operations.

What the job comes with

  • Base salary: $140,000 – $170,000. This range reflects our Canada-wide compensation scale. Final offers may be adjusted based on the candidate’s region to align with local market conditions.
  • Annual bonus program.
  • LTIP program, share in Workleap’s long-term growth.
  • RRSP + Family health insurance + telemedicine + annual wellness budget.
  • Flexible vacation policy.
  • Remote work, with access to our Montreal office.
  • In-person gatherings twice a year.
  • Claude access, for everyone.

What drives us

At Workleap, we build software that sits at the center of how people experience work, every day, at every level.

We move fast. Priorities shift, decisions get made with the information we have, and we iterate. If you thrive on intensity and ambiguity doesn’t slow you down, you’ll feel right at home.
We’re builders. We do what it takes to move forward. AI is part of our toolkit. We use it to go faster and decide smarter, not to replace judgment.

If you want real impact and a place where your decisions matter, this is it.

How we hire

Transparency is how we hire — for you as much as for us.

Here’s how it works: a first call with a recruiter, then a virtual interview with the hiring manager. You’ll then complete a take-home case study, followed by a meet with future colleagues to discuss it together. Depending on the role, the process may vary slightly — your recruiter will walk you through it on your first call.

We use AI to support certain steps of the process, but every hiring decision remains human.

We can’t wait to meet you.

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This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

5/5
EasyHard

Pace & Pressure

5/5
RelaxedFast-paced

Autonomy Level

5/5
GuidedFull ownership

Communication Load

5/5
IndependentCollaborative
AI insightThis is a senior, ambiguous build-from-zero role involving reliable production deployment of non-deterministic multi-agent systems. It requires architectural leadership across AI quality, security, evaluation, cost, and product adoption.

Salary analysis

Estimated compensation compared with the broader CA market for similar roles.

Estimated job median (per year)Market rate
C$155,000
CA market range (per year)C$140k–C$190k
AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. The employer discloses a base salary of CAD 140,000 to CAD 170,000. The yearly cadence is inferred from the full-time senior role, Canadian compensation context, and range magnitude; it is not explicitly stated as annual. A reasonable estimated Canadian market range for a senior production AI/agentic systems developer is CAD 140,000 to CAD 190,000 per year, excluding bonus and long-term incentives.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
How would you design a production multi-agent workflow for creating a product brief?

I would begin by defining a narrow workflow with explicit inputs, outputs, ownership, and human approval gates. I would separate responsibilities among specialized agents, use structured schemas for context transfer, and provide grounded access to approved sources such as Jira, Confluence, and product analytics. I would then instrument the workflow for quality, latency, cost, and failure modes before expanding its scope.

How do you evaluate a non-deterministic agent system before production release?

I create a representative evaluation set based on real user tasks, including expected outputs, edge cases, and safety-sensitive scenarios. I measure task success, factual grounding, tool-use correctness, latency, cost, and user-edit distance, then run regression evaluations whenever prompts, models, tools, or orchestration change. Human review remains essential for high-impact decisions and for calibrating automated metrics.

What factors guide your choice of model for a specific agent task?

I consider task complexity, required reasoning quality, context length, latency, privacy requirements, reliability, and cost. A simple extraction or classification task may use a smaller, faster model, while synthesis across complex product context may justify a stronger model. I validate the decision with measured performance on representative evaluations rather than relying on assumptions.

How would you prevent context loss and uncontrolled cost growth between specialized agents?

I would pass compact, structured artifacts rather than raw conversation histories, with clear contracts for what each agent needs and produces. I would use retrieval selectively, summarize durable state, enforce context and tool-call budgets, and trace token consumption per workflow step. Evaluation data would help identify where additional context improves outcomes and where it only increases cost.

Describe how you would encourage Product Managers and engineers to adopt a new agent workflow.

I would co-design the workflow with the people who perform the work, starting with a small high-friction use case and measurable success criteria. Early releases would preserve human control, make outputs easy to inspect and revise, and incorporate feedback quickly. I would document patterns, share practical examples, and provide reusable tooling so adoption can extend beyond the initial team.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
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