AI Engineer

Fulfillment IQ; Toronto; On-site; CAD 135,000 - 170,000; Full time

Job summary

Fulfillment IQ in Toronto is seeking a senior AI Engineer to own the design and delivery of production-ready LLM systems, including RAG, agents, and APIs. You will shape architecture with high ownership and fast-moving development.

The ideal candidate has hands-on experience shipping LLM features to production, strong Python backend skills, and a track record of optimizing for cost and latency while collaborating across teams.

Qualifications

• Strong backend/software engineering foundation (Python, APIs, system design).
• Proven experience shipping LLM-powered features to production.
• Deep expertise in RAG systems, LLM evaluation methodologies, prompt engineering at API level, and agent architectures (ReAct, tool calling, planning loops).
• Strong understanding of cost, latency, and scalability trade-offs.
• Ability to work independently in ambiguous, fast-moving environments.

Responsibilities

• Design and build production-grade LLM systems (RAG, agents, APIs).
• Architect systems that minimize rework in fast-evolving environments.
• Own end-to-end delivery of critical AI features.
• Define and implement evaluation frameworks.
• Optimize systems for cost, latency, and reliability.
• Collaborate across teams and provide technical guidance.

Skills

Python

APIs

System design

LLM in production

RAG systems

LLM evaluation

Prompt engineering

Agent architectures

Cost optimization

Latency optimization

Education

Bachelor's or master's in computer science

Tools

Langfuse

LangSmith

vLLM

llama.cpp

Job description

General Information:

Job Title: AI Engineer
Location:Toronto, ON (Onsite/Hybrid)
Job Type:Full-Time
Hiring Timeline: Immediate
Reporting Line: Head of R&D
Existing Vacancy: Yes
Salary Range: 135K – 170K CAD per year (negotiable)

About Fulfillment IQ (FIQ):

Fulfillment IQ is a supply chain engineering and transformation company that helps brands, retailers, and 3PLs design, build, and scale high-performancelogisticsoperations.

We work at the intersection of strategy, operations, and technologywhere wesolvecomplex, real-world problems across warehouse design, automation, order management, transportation, and end-to-end supply chain execution.

Our teams combine deep domainexpertisewith strong technical capability, delivering outcomes through consulting, systems implementation, and proprietary platforms that accelerate time-to-value and reduce delivery risk.

If you enjoy working in complex environments, partnering closely with clients, and seeing your work make a tangible impact on how global commerce moves,thisistheplace where your skills and judgment trulycome to life.

Role Overview:

This is a high-impact, senior engineering role, where engineers are expected to operate with significant ownership and minimal oversight. The role focuses on building production-ready AI systems in an environment where speed, correctness, and architectural decisions have long-term implications.

Ideal Candidate’s Profile:

A seasoned AI engineer (ninja-level) with hands-on experience in developing and deploying real LLM systems, who excels in environments with significant ownership responsibilities and values impactful work more than structured, low-risk settings.

Individuals driven by ownership, autonomy, and the opportunity to build from the ground up (rather than being a small cog in a large organization) will thrive here.

Responsibilities & Expectations:

Key Responsibilities:

• Design and build production-grade LLM systems (RAG, agents, APIs)
• Architect systems that minimize rework in fast-evolving environments
• Own end-to-end delivery of critical AI features
• Define and implement evaluation frameworks
• Optimize systems for cost, latency, and reliability
• Collaborate across teams where needed
• Provide technical guidance where applicable (especially for less experienced engineers on adjacent teams)

Must-Haves (non-negotiables):

• Strong backend/software engineering foundation (Python, APIs, system design)
• Proven experience shipping LLM-powered features to production (non-negotiable)
• Deep expertise in:
  • RAG systems (advanced retrieval + evaluation)
  • LLM evaluation methodologies (golden sets, regression testing)
  • Prompt engineering at API level
  • Agent architectures (ReAct, tool calling, planning loops)
• Strong understanding of trade-offs (cost, latency, scalability)
• Ability to work independently in ambiguous, fast-moving environments

Nice-to-Have:

• Fine-tuning experience (LoRA, SFT, DPO)
• Inference stack experience (vLLM, TGI, llama.cpp)
• Observability tooling (Langfuse, LangSmith)
• Prior experience in early-stage or high-ownership teams

• Public work (GitHub, blogs, talks) demonstrating depth

Education:

• Bachelor's or master's degree in computer science or a related discipline

Technical Skills:

• Advanced Python and backend engineering