AI Agent Engineer

Nuxt; Toronto; On-site; CAD 110,000 - 200,000; Full time

Job summary

Gigi in Toronto is hiring an AI Agent Engineer to own end-to-end development of agents and workflows behind the AI media manager. You’ll design APIs, model data, and integrate services while focusing on how LLMs reason, use tools, retain context, and take actions for customers.

You’ll build across agent logic, backend services, data, and the customer-facing product. You don’t need to be an AI researcher; strong engineers with hands-on work with LLMs and agents are encouraged to apply.

Qualifications

• Proven software engineering experience with backend or full-stack work.
• Hands-on experience with LLMs or agents and building end-to-end features.
• Ability to explain tooling choices and trade-offs clearly.

Responsibilities

• Own end-to-end agent capabilities from data & tools to UI touchpoints.
• Develop with AI at the core of workflows, plan and review code with AI agents.
• Define where autonomous actions vs human oversight are used and assess risks.
• Balance probabilistic reasoning with deterministic execution in features.
• Build reliable agentic systems with memory, retrieval, and guardrails.
• Turn customer workflows into shipped features and iterate from feedback.
• Engage with customers, translate feedback into product improvements.

Skills

Backend development

Full stack

LLMs & agents

AI coding tools

System design

Code review

Customer collaboration

Tools

LangGraph

LangChain

Python

TypeScript

Vue

Nuxt

PostgreSQL

Redis

AWS

Job description

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 elevate 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 you’re weaker in.
• Experience using, or a strong desire to become highly effective with, AI coding tools such as Codex, Claude Code, Cursor, or similar tools. You do not need to be an expert on day one, but you should be excited to use them every day—and strong enough technically to review their work, reject weak abstractions, catch missing edge cases, and remain accountable for what ships.
• Curiosity about how LLMs change software products and the way software gets built. You should be excited to develop good judgment around where AI belongs—and where conventional software is the better answer.
• Judgment over specification. You can make a reasoned call, ship it, and iterate fast when it’s wrong.
• Speed and ownership. LLMs changed what “fast” means for a small team. You should be comfortable using AI coding agents to move quickly while maintaining a high bar for architecture, correctness, and maintainability.

What you don’t need

• You do not need an AI research background or experience training foundation models. We build products around existing models, combining them with tools, data, business logic, interfaces, and production infrastructure.
• You do not need prior experience with every agent framework or concept we use. We expect strong engineers to learn LangGraph, agent orchestration, evaluations, retrieval, and tool use on the job.
• You do not need prior advertising or Amazon DSP experience. Our team will help you learn the domain.

Bonus points if

• You've built with LLMs in production: evals, guardrails, tool use, retrieval, agent orchestration, cost and latency work.
• You've built evaluation datasets or used production traces and customer feedback to improve an agent's behavior.
• You've worked in advertising or ecommerce.
• You've built real‑time data pipelines at meaningful volume, or been early enough at a startup to know what the first 20 people actually do.
• You've operated production systems where correctness, permissions, auditability, or customer trust mattered.

The hard parts

We'd rather you know this before you apply.

• We work hard. We are trying to build a generational company, and early employees should expect intensity, ambiguity, and high standards.
• Cash compensation is competitive for our stage, but we will not win every late-stage salary comparison. The upside is meaningful ownership and equity in a company growing quickly.
• We throw out working code when the underlying abstraction is wrong. Some of what you build will not survive, and you'll be the one who calls it.
• Nobody is siloed. You'll write frontend, debug backend, think through product, join customer calls, and solve whatever problem is blocking the customer.

Compensation and equity

• Base salary: $110k to $200k depending on experience level.
• Meaningful equity. We want to be as generous with equity as possible, and top performers can expect equity top-ups early and often.

Life at Gigi

• In‑office culture. We work together downtown Toronto, but makers (i.e. engineers) can choose to work where they are most productive on Wednesdays.
• Catered lunch every day from Toronto's best spots. Current rotation: Impact Kitchen, iQ Food Co., ChopHop, and Cumbrae's.
• Real snacks: Greek yogurt, Mateína yerba mate, Barebells protein bars, Midday Squares. Tell us what you want and we'll add it.
• Top‑of‑the‑line Apple equipment.
• Comprehensive health and dental coverage.