AI Agent Engineer

Gigi is the agentic operating system of enterprise media buying. Our first product is the AI media manager for Amazon DSP. Since launching it in summer 2025, we've grown to manage hundreds of millions in advertising spend on behalf of the largest agencies in the world. We're backed by top venture investors Golden Ventures and Aperiam Ventures. Our founding team has spent 15+ years building in Amazon Ads and ecommerce, including a prior $100M+ exit. We're hiring an AI Agent Engineer to join our engineering team in Toronto and own end-to-end development of the agents and workflows behind Gigi, our AI media manager. This is fundamentally a software engineering role. Much of the work will feel familiar: designing APIs, modelling data, integrating services, writing tests, debugging production issues, and making architectural decisions. Your focus will be how LLMs reason, use tools, retain context, and take actions on behalf of customers—and the software that makes those actions reliable. You do not need to be an AI researcher or have years of professional LLM experience. Strong software engineers who have started building with LLMs and agents, and want to take that work further, should apply. You'll use tools such as Codex, Claude Code, and Cursor as a core part of how you work, while building across agent logic, backend services, data, and the customer-facing product. As an early employee, you will have a meaningful role in shaping our product, engineering culture, and company.

What you'll do

Ship agents end to end.

You'll own capabilities from the data and tools an agent uses through its reasoning and execution to the interface a media buyer actually touches. Examples include turning a brief into a campaign, investigating performance, and carrying out approved changes. Our stack includes Python, FastAPI, LangGraph and LangChain; TypeScript, Vue and Nuxt; Java and Spring Boot; PostgreSQL, pgvector, Redis and NATS; and AWS.

Build with AI as a core part of your development workflow.

Use Codex, Claude Code, Cursor, and other coding agents to help plan, implement, test, debug, and review software. You are still responsible for the architecture, quality, and correctness of what gets shipped.

Build the agent's judgment, not just its plumbing.

Decide what Gigi should do autonomously and what it must escalate to a human, then score actions on risk and confidence so recommendations read differently when the data is thin.

Draw the line between probabilistic and deterministic work.

LLMs for reasoning, structured software for execution. Knowing where that boundary sits on any given feature is a daily call, not a settled architecture.

Build reliable agentic systems.

Design tools, retrieval, memory, orchestration, guardrails, and human approval flows that make Gigi dependable enough to take action inside real advertising accounts. Handle incomplete context, tool failures, and workflows that need to pause and resume. Prior experience with all of these is not expected.

Evaluate and improve agent behavior.

Turn real customer workflows and production failures into evaluations and regression tests. Inspect traces, experiment with prompts and context, and measure whether changes improve the agent's ability to complete the task correctly.

Work directly with customers.

You'll sit in on calls, read transcripts, and take feedback straight from the media buyers using the product. You'll turn ambiguous customer problems into shipped product without waiting for a perfect spec.

What success looks like

First 30 days.

Your code is in production. You understand what a media buyer's day actually involves and why the product is shaped the way it is.

Six months.

You own an agent capability outright, including its evaluations and production behavior. When it fails or a customer asks it to do something new, you're the person who decides what happens.

Twelve months.

You've shipped something we could not have built without you, expanded what customers can confidently delegate to Gigi, and thrown out at least one thing you built because the abstraction was wrong.

What you'll bring

A track record of building and shipping production software used by real customers. We care more about what you have built and owned than an exact number of years.

Strong software engineering experience, with depth in backend or full stack development. System design, APIs, data models, testing, debugging, and operating production software are central to this role.

Hands-on experience building with LLMs or agents, whether at work, through open source, or in a substantial personal project. You can explain how it works, where it fails, and how you would improve it.

Comfort across the stack. Real depth somewhere, no allergy to the parts y