Position Name – AI Tech Lead Type of hiring – Subcon Location – Brampton, ON or Toronto, ON (Onsite) : We are looking to hire an AI Tech Lead to own and drive the architectural foundation of our AI ecosystem.

What You Will Do Architectural Leadership — Define and evolve the end‑to‑end architecture for RAG pipelines, agent frameworks, and distributed inference systems. Prioritize scalability, latency, and UX‑aligned output determinism.

Rigorous

Evaluation — Build custom evaluation harnesses for AI agents, RAG, and LLM reasoning. Move beyond out‑of‑the‑box metrics with domain‑specific scoring, adversarial tests, and regression suites. Advanced AI Paradigms — Continuously integrate cutting‑edge methodologies (structured reasoning, tool‑use optimization, memory systems, multi‑agent collaboration).

Technical

Mentorship — Coach developers on AI security, token‑economics, UX patterns, and safe deployment practices. Establish coding standards and architectural guardrails.

Required Qualifications Proven leadership delivering production‑grade ML/AI systems. Strong foundations in vector math, LLM internals, embeddings, agent frameworks, and evaluation science. Knowledge of Google’s GECX is a robust bonus. Expertise in systems‑level programming, distributed architecture,



and performance‑critical code.

Skillset Requirements Systems

Architecture — Expertise designing distributed AI systems, multi‑agent frameworks, and scalable RAG pipelines. LLM Internals — Deep understanding of transformer mechanics, attention patterns, tokenization, and inference optimization.

Evaluation

Science — Ability to design adversarial tests, regression suites, and domain‑specific scoring functions. AI Security — Knowledge of jailbreak prevention, prompt‑injection defense, secure tool‑use, and zero‑trust agent routing. Tokenomics — Ability to optimize token usage, context windows, and cost‑latency trade-offs.

Distributed

Systems — Experience with load balancing, sharding, concurrency, and high‑availability inference clusters. Observability — Familiarity with tracing, logging, telemetry, and agent‑level debugging. DevOps for AI — CI/CD for model updates, containerization, GPU orchestration, and rollout strategies.

Mentorship & Leadership — Ability to enforce engineering discipline, code quality, and architectural guardrails. Responsible AI & Governance — Guardrail design, content-safety policy, and audit/traceability for regulated deployments.

📌 AI Tech Lead (Toronto)
🏢 Yochana
📍 Toronto