AI role · Open offer

Senior LLM Engineer — enterprise RAG

Cohere

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4 skills2 tools

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At a glance

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

Employment: Full-time
Workplace: Hybrid
Location: 88 Queens Quay W, Toronto, ON M5J 0B6, Canada · Hybrid
Compensation: CAD 150k–200k/year + equity
Experience level: Senior
Team: LLM platform
Benefits & perks

About this role

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

Cohere builds enterprise-grade language models and APIs used globally from our Toronto headquarters.

Join as a Senior LLM Engineer to ship RAG, embeddings, and fine-tuning workflows for regulated customers.

What you will do

Ship production LLM and retrieval systems 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

Core capabilities—LLMs, automation, analytics, product, or domain expertise.

PythonLangChainOpenAI APIPyTorch

Tools & stack

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

Vector DBsDocker

You're a strong fit if…

You've shipped real work with AI—not only experimented in isolation.
Several listed skills or tools show up in your recent projects.
You can explain trade-offs (quality, cost, latency, safety) in plain language.

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

RAG or embedding pipeline with eval metrics and cost notes.

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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4 skills · 2 projects · 2 roles · 3 use cases

Built

Regulated RAG rollout
Multi-tenant pipeline: chunking, ACL-aware retrieval, audit logs, and human approval for outbou…
Demo Repo Link
Eval harness
Golden sets + LLM-as-judge with calibration against human labels; CI gate before prod prompt ch…
Repo

Top skills

Python

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