We are looking to hire AI Tech Lead who 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 secure deployment practices. Establish coding standards and architectural guardrails.

Required Qualifications
Proven leadership delivering production grade ML/AI systems.
Solid foundations in vector math, LLM internals, embeddings, agent frameworks, and evaluation science.
Knowledge of Google's GECX is a solid 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.

📌 Ai Tech Lead Brampton
🏢 J M Group
📍 Brampton