Position : AI Technical Consultant

Location : Brampton, Ontario, Canada

Duration : Long Term Contract

Detailed JD (Roles and Responsibilities)

:

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 robust 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.
- Toke nomics: 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 Technical Consultant @ Brampton, Ontario, Canada
🏢 Burgeon IT Services
📍 Brampton