Job Opportunities API

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-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) 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 LLM systems (RAG, agents, prompting, evaluation) API design and system architecture Docker, Git, CI/CD Understanding of inference systems and scaling Soft Skills: High ownership and accountability Ability to operate in ambiguity (“build while flying”) Strong decision-making and trade-off analysis Clear communication with cross-functional teams What Success Looks Like in the First 90 Days: By the end of Month 1: D