AI Tech Lead
- Brampton, ON, Canada
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Original job description
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.
More about this job
Responsibilities
Lead the architecture and evolution of AI systems, including RAG pipelines, agent frameworks, and distributed inference, with a focus on scalability, latency, and predictable outputs. 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, strong knowledge of LLM internals, embeddings, vector mathematics, agent frameworks, and evaluation methods. Also requires expertise in distributed architecture, performance-critical systems, AI security, observability, AI DevOps, token-cost optimization, responsible AI governance, and technical mentorship.
Skills
- AI Systems Architecture
- Retrieval-Augmented Generation (RAG)
- Agent Frameworks
- Distributed Inference Systems
- Large Language Model Internals
- Evaluation Science
- AI Security
- Distributed Systems
- Performance Optimization
- Observability
- AI DevOps
- Token Optimization
- Responsible AI Governance
- Technical Mentorship
Visa sponsorship
Not detected in the job text
Categories
- Technology
- Software
- Engineering
- Management & Leadership
- Data & Analytics
Keywords
- Artificial Intelligence
- Machine Learning
- RAG Pipelines
- Agent Frameworks
- Distributed Inference
- Large Language Models
- Transformer Mechanics
- Attention Patterns
- Tokenization
- Embeddings
- Vector Mathematics
- Evaluation Harnesses
- Adversarial Testing
- Regression Suites
- Structured Reasoning
- Tool Use
- Memory Systems
- Multi-Agent Collaboration
- Prompt Injection Defense
- Jailbreak Prevention
- Zero-Trust Agent Routing
- Token Economics
- Load Balancing
- Sharding
- High Availability
- Tracing
- Telemetry
- CI/CD
- GPU Orchestration
- Responsible AI