AI Tech Lead
- Brampton, ON, Canada
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- English
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Original job description
We are looking to hire AI Tech Lead who owns the architectural backbone of our AI ecosystem.
What You Will Do
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.
- 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.
- 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
Define and evolve the architecture for RAG pipelines, agent frameworks, and distributed inference systems, prioritizing scalability, latency, and predictable user experiences. Build rigorous evaluation systems, integrate advanced AI methods, and mentor developers on security, cost optimization, safe deployment, and engineering standards.
Requirements
Requires proven leadership delivering production-grade AI or ML systems, with strong knowledge of vector mathematics, LLM internals, embeddings, agent frameworks, and evaluation methods. Candidates should also have expertise in distributed architecture and performance-critical systems, plus knowledge of AI security, token optimization, observability, AI DevOps, and responsible AI governance.
Skills
- AI Systems Architecture
- Distributed Systems
- RAG Pipelines
- LLM Internals
- Agent Frameworks
- Evaluation Science
- AI Security
- Token Optimization
- Inference Optimization
- Observability
- AI DevOps
- Responsible AI
- Technical Mentorship
Visa sponsorship
Not detected in the job text
Categories
- Technology
- Software
- Engineering
- Management & Leadership
- Data & Analytics
Keywords
- RAG Pipelines
- Agent Frameworks
- Distributed Inference
- Vector Mathematics
- Large Language Models
- Transformer Mechanics
- Attention Patterns
- Tokenization
- Embeddings
- Evaluation Harnesses
- Adversarial Testing
- Regression Suites
- Structured Reasoning
- Tool Use Optimization
- Memory Systems
- Multi-Agent Collaboration
- AI Security
- Jailbreak Prevention
- Prompt Injection Defense
- Zero-Trust Agent Routing
- Token Economics
- Load Balancing
- Sharding
- High Availability
- Tracing
- Telemetry
- CI/CD
- Containerization
- GPU Orchestration
- Content Safety