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 rapid-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

📌 AI Engineer (Toronto)
🏢 Fulfillment IQ
📍 Toronto