AI role · Open offer

LLM Engineer — customer automation

Ada

Full-time
Senior
Conversational AI
Hybrid
425 Adelaide St W, Toronto, ON M5V 3C1, Canada · Hybrid
Toronto, Canada
CAD 115k–150k/year + equity

This listing is optimized for : clear skills and tools so candidates know if they fit before they apply—and so you spend less time screening mismatches.

At a glance

How you'll work, where, and key listing dates.

Employment: Full-time
Workplace: Hybrid
Location: 425 Adelaide St W, Toronto, ON M5V 3C1, Canada · Hybrid
Compensation: CAD 115k–150k/year + equity
Experience level: Senior
Team: Conversational AI
Benefits & perks

About this role

What the team is looking for and how this position fits.

Ada is hiring a Senior LLM Engineer — customer automation on the Conversational AI team (Hybrid · Toronto, Canada). This is a hands-on role in LLM & generative AI—not a generic “AI enthusiast” post.

You will own LLM features, retrieval, agents, and evaluation end to end: problem framing, offline evaluation, safe rollout, and iteration from production signals. Success is measured with clear metrics (quality, latency, cost, or business KPIs)—not slide decks alone.

Ada automates customer service for global brands with conversational AI built in Toronto.

Own LLM flows, tool use, and quality metrics for enterprise support automation.

What you will do

Ship production conversational AI at scale with offline evals, monitoring, and safe rollout.
Work with product and domain experts on requirements and trade-offs.
Improve data pipelines, labeling, and metrics tied to business KPIs.
Document runbooks for on-call and customer-facing teams.

What we look for

Strong Python and modern ML/LLM stack with shipped features.
SQL, experimentation, and clear communication to stakeholders.
Eligible to work in the listed country; professional English required.

Skills we're hiring for

What each capability means for this role—not a keyword checklist.

Python
Production Python for data pipelines, model code, APIs, and automated tests—not notebook-only workflows.
LangChain
Orchestrating LLM chains, tools, and retrieval with observable steps and failure handling.
OpenAI API

Tools & stack

Platforms and products mentioned for this role (APIs, IDEs, vector DBs, CRMs, design tools, etc.).

DockerTool
Containerized services and reproducible dev environments for ML and LLM workloads.

You're a strong fit if…

Tailored to this listing—use it as a self-check before you apply.

You can show evidence in your application: Dialog or LLM automation with CSAT / resolution metrics.
Several of these show up in recent shipped work—not only on your CV: Python, LangChain, OpenAI API, and Docker.
You have owned LLM features, retrieval, agents, and evaluation in production: debugging live issues, running postmortems, and iterating from real user or business signals.
You are comfortable with the hybrid rhythm (Hybrid · Toronto, Canada)—onsite collaboration when it matters, deep work when remote.
You explain trade-offs (quality, cost, latency, safety) in plain language—with numbers or examples, not buzzwords.
You read the full listing (comp: CAD 115k–150k/year + equity, benefits in At a glance) and your expectations align.

Apply

Candidate accounts only: add your headline and bio on , and optionally a CV link or PDF so recruiters see your full résumé. Then write a short pitch below—applications use your login email.

What the employer asked for

Dialog or LLM automation with CSAT / resolution metrics.

Register, add a headline and bio, then you can attach a CV link or PDF on My account before you apply. That keeps applications tied to your profile and makes it easier for employers to review your background.

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