AI/ML Tech Lead
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We are looking to hire AI/ML Tech Lead at Brampton/ Toronto, ON (Hybrid) owns the architectural backbone 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 strong 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.