AI Engineer
$135,000–$170,000 year
HybridToronto, Ontario, Canada
Toronto, Ontario, CanadaHybridFull Time$135,000–$170,000 yearBachelors DegreeSmall
Full TimeBachelors DegreeSmall
Job Summary
Design and build production-grade LLM systems including RAG, agents, and APIs while architecting solutions that minimize rework in fast-evolving environments. Own end-to-end delivery of critical AI features, define evaluation frameworks, and optimize systems for cost, latency, and reliability. Provide technical guidance to adjacent teams and operate with minimal oversight in ambiguous, high-stakes projects. Deliver measurable impact on system reliability and efficiency within the first 90 days by identifying architectural gaps and driving improvements.
Required Qualifications
- 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)
- Deep expertise in 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
- Bachelor's or master's degree in computer science or a related discipline
- 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
- High ownership and accountability
- Ability to operate in ambiguity
- Strong decision-making and trade-off analysis
- Clear communication with cross-functional teams
- Location: Toronto, ON (Onsite/Hybrid)
- Job Type: Full-Time
Desired Qualifications
- 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
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