Fulfillment IQ
supply chain engineering
AIEngineer
Neural analysis suggests this role is
optimal for Senior candidates.
“AI Engineer at Fulfillment IQ. Skills: LLM systems, RAG systems, Agent architectures, LLM evaluation, Prompt engineering, backend/software engineering foundation, system design. Design and build production-grade LLM systems (RAG, agents, APIs). Architect systems that minimize rework in fast-evolving environments”
What You'll Achieve.
Deliver measurable impact (system reliability, quality, or efficiency); Optimize systems for cost, latency, and reliability
Industry & Context.
solve complex, real-world problems; decision-making and trade-off analysis
business travel coverage
What They're Looking For.
Must Have
backend/software engineering foundation (Python, APIs, system design), Proven experience shipping LLM-powered features to production, Deep expertise in RAG systems (advanced retrieval + evaluation), Deep expertise in LLM evaluation methodologies (golden sets, regression testing), Deep expertise in Prompt engineering at API level, Deep expertise in Agent architectures (ReAct, tool calling, planning loops), understanding of trade-offs (cost, latency, scalability), Ability to work independently in ambiguous, fast-moving environments, Advanced Python and backend engineering, LLM systems (RAG, agents, prompting, evaluation), API design and system architecture, Docker, Git, CI/CD, Understanding of inference systems and scaling
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
What You'll Do.
Design and build production-grade LLM systems (RAG
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
Collaborate across teams where needed
Provide technical guidance where applicable
How You'll Work.
Team & Collaboration
Collaborate across teams where needed; Provide technical guidance where applicable (especially for less experienced engineers on adjacent teams); Clear communication with cross-functional teams; Work alongside talented engineers, product leaders, and award-winning domain experts
Communication Scope
Clear communication with cross-functional teams
Process & Methodology
Own end-to-end delivery of critical AI features, Operate with significant ownership and minimal oversight, Independent execution
Full Job Description
**Description** **General Information:** **Job Title:** AI Engineer **Location:** Toronto, ON (Onsite/Hybrid) **Job Type:** Full-Time **Reporting Line:** Head of R&D **Salary Range:** CAD 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-performance logistics operations. We work at the intersection of strategy, operations, and technology where we solve complex, real-world problems across warehouse design, automation, order management, transportation, and end-to-end supply chain execution. Our teams combine deep domain expertise with 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, this is the place where your skills and judgment truly come 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
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