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):

- Solid 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

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📌 AI Engineer (Toronto)
🏢 Fulfillment IQ
📍 Toronto