Senior Engineer, Applied AI

General Information:

Job Title: Senior Engineer, Applied AI
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):

• 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:

• Deeply understand Crosstalk/Zync architecture and ongoing projects
• Contribute meaningfully to ongoing systems (not just onboarding tasks)
• Identify gaps or risks in current implementations

By the end of Month 2:

• Own and deliver a critical feature or system component end-to-end
• Improve an existing system (performance, evals, or architecture)
• Demonstrate strong independent execution

By the end of Month 3:

• Act as a trusted senior engineer on the team
• Drive architectural decisions