General Information:Job Title: AI EngineerLocation:Toronto, ON (Onsite/Hybrid)Job Type:Full-TimeHiring Timeline: ImmediateReporting Line: Head of R&DExisting Vacancy: YesSalary 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 prospect 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 environmentsOwn end-to-end delivery of critical AI featuresDefine and implement evaluation frameworksOptimize systems for cost, latency, and reliabilityCollaborate across teams where neededProvide 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 levelAgent architectures (ReAct, tool calling, planning loops)Strong understanding of trade-offs (cost, latency, scalability)Ability to work independently in ambiguous, fast-moving environmentsNice-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 teamsPublic work (GitHub, blogs, talks) demonstrating depthEducation:Bachelor's or master's degree in computer science or a related disciplineTechnical Skills:Advanced Python and backend engineering #J-18808-Ljbffr

📌 Ai Engineer (Toronto)
🏢 Fulfillment IQ
📍 Toronto