AI Tech Lead

Job summary

Lead the architecture and evolution of production AI systems, including RAG pipelines, agent frameworks, and distributed inference, with a focus on scalability, latency, and predictable user experiences. Develop rigorous evaluation and security practices, integrate advanced AI methods, and mentor developers while establishing engineering standards and architectural guardrails.

Job details

This job posting is for an existing, active vacancy and we are looking to hire AI Tech Lead at Brampton/ Toronto, ON 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.

What you’ll do

Lead the architecture and evolution of production AI systems, including RAG pipelines, agent frameworks, and distributed inference, with a focus on scalability, latency, and predictable user experiences. Develop rigorous evaluation and security practices, integrate advanced AI methods, and mentor developers while establishing engineering standards and architectural guardrails.

Requirements

Requires proven leadership delivering production-grade AI or ML systems, with strong knowledge of LLM internals, embeddings, vector mathematics, agent frameworks, and evaluation methods. Also requires expertise in distributed architecture and performance-critical systems, plus capabilities in AI security, token and cost optimization, observability, AI DevOps, responsible AI, and mentorship.

Listed skills

• Évaluation · Preferred
• Production · Preferred
• Technical · Preferred
• CI/CD · Preferred
• Mentorship · Preferred
• Collaboration · Preferred
• Leadership · Preferred
• safety · Preferred
• mechanics · Preferred
• Windows · Preferred
• Audit · Preferred
• discipline · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Systems Architecture
• Distributed Systems
• RAG Pipelines
• Agent Frameworks
• LLM Internals
• Evaluation Science
• AI Security
• Token Optimization
• Inference Optimization
• Observability
• AI DevOps
• GPU Orchestration
• Responsible AI
• Technical Mentorship

Job areas

• Technology
• Software
• Engineering
• Management & Leadership
• Data & Analytics

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