We're looking for an AI Technical Lead to own the architectural backbone of an enterprise AI ecosystem. The ideal candidate brings deep expertise in RAG systems, agentic orchestration, and LLM internals, and can lead a team of developers while keeping the AI stack scalable, secure, transparent, and production-ready.
What you'll do
- Define and evolve end-to-end architecture for RAG pipelines, agent frameworks, and distributed inference systems with a focus on scalability, latency, and output determinism
- Build custom evaluation harnesses for AI agents, RAG pipelines, and LLM reasoning including domain-specific scoring, adversarial tests, and regression suites
- Continuously integrate cutting-edge AI methodologies including structured reasoning, tool-use optimization, memory systems, and multi-agent collaboration
- Design and enforce AI security practices including jailbreak prevention, prompt-injection defense, secure tool use, and zero-trust agent routing
- Optimize token usage, context windows, and cost-latency trade-offs across AI workloads
- Establish observability practices including tracing, logging, telemetry, and agent-level debugging
- Own CI/CD for model updates, containerization, GPU orchestration, and deployment rollout strategies
- Design guardrails, content-safety policies, and audit and traceability frameworks for regulated deployments
- Coach and mentor developers on AI security, tokenomics, UX patterns, and safe deployment practices
- Establish coding standards and architectural guardrails across the AI engineering team
What you bring
- Proven track record delivering production-grade ML/AI systems in a technical lead capacity
- Strong foundations in vector mathematics, LLM internals, embeddings, agent frameworks, and evaluation science
- Deep understanding of transformer mechanics, attention patterns, tokenization, and inference optimization
- Expertise in distributed systems design including load balancing, sharding, concurrency, and high-availability inference clusters
- Experience with systems-level programming and performance-critical code
- Strong knowledge of responsible AI and governance practices for regulated deployments
- Ability to enforce engineering discipline and code quality across a development team
Nice to have
- Knowledge of Google GECX frameworks
- Prior experience in enterprise AI deployments within regulated industries
- Familiarity with GPU orchestration and large-scale inference optimization
Work setup
- Onsite in Brampton, ON
- 6 to 12 month contract
- English proficiency required
