Position Name – AI Tech Lead
Type of hiring – Subcon
Location – Brampton, ON or Toronto, ON (Onsite)
Job Description:
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 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.
