Role : AI Lead Consultant

Location : Brampton, ON

- A visionary systems-level architect who treats AI as an engineering discipline, not an
- experimentation playground. This role sets the technical north star for generative AI
- programs, ensuring every agent, pipeline, and evaluation framework is built with rigor,
- determinism, and long-term maintainability.
- About the Role
- The AI Technical Lead owns the architectural backbone of AI ecosystem.

You will steer

- the design of RAG systems, agentic orchestrations, evaluation harnesses, and
- deployment patterns that scale across enterprise workloads. You will mentor
- developers, enforce engineering discipline, and ensure the AI stack remains
- transparent, debuggable, and future-proof.
- 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 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- Technical Lead

📌 AI Lead Consultant (Only on (Brampton)
🏢 Ampstek
📍 Brampton