We have 2 urgent openings for AI Technical Lead and AI Developer, below are the job description for both roles.

About the Role

1 We are looking to hire AI Developer at Brampton, ON (Onsite), immediately who has strong experience in Coding, RAG Pipelines, Python. A hands-on builder who writes the native code powering our RAG pipelines, agentic workflows, and context-engineering systems. You will work close to the metal: no heavy frameworks, no magic abstractions: just transparent, debuggable AI infrastructure.

Responsibilities

Implement the mechanical core of our AI features: chunking logic, embedding flows, retrieval algorithms, agent state machines, and prompt-construction engines. Ensure every component is observable, testable, and optimized for enterprise workloads. Build RAG Pipelines: Implement custom chunking, embeddings, hybrid search, re-ranking, and retrieval logic tailored to domain-specific semantics. Agentic Orchestration: Build multi-step agents with working memory, tool execution, state tracking, and deterministic control flows. Context Engineering: Optimize prompts, context packing, and token-economics to maximize reasoning quality while minimizing latency and cost.

Qualifications

Strong software engineering fundamentals with intermediate Python. Experience building transparent AI systems using standard libraries and HTTP clients. Hands-on FastAPI server development. Knowledge of Google’s GECX. Experience with structured extraction (JSON schemas, Pydantic) and advanced prompting.

Required Skills

Native RAG Implementation: Custom chunking, embeddings, hybrid search, re-ranking, and retrieval logic. Agentic Programming: Building tool-use flows, working memory, state machines, and deterministic agent orchestration. Prompt Engineering: Crafting structured prompts, multi-shot reasoning scaffolds, and domain-specific context packing. Python Engineering: Strong fundamentals, async programming, concurrency, and performance tuning. FastAPI: Building transparent, debuggable AI microservices. Structured Extraction: JSON schema design, Pydantic models, and deterministic extraction patterns. LLM Tooling: Experience with HTTP clients, raw API calls, and minimal-framework AI development. Testing & Debugging: Unit tests for agents, RAG regression tests, and prompt-level debugging. Observability: Instrumenting tracing, logging, and token/latency metrics for agents and RAG components.

2 We are looking to hire AI Technical Lead at Brampton, ON (Onsite), immediately who has strong experience in RAG Systems & AI Agents.

About the Role

The AI Technical Lead owns the architectural backbone of our 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 our AI stack remains transparent, debuggable, and future-proof.

Responsibilities

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.

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 strong bonus. Expertise in systems-level programming, distributed architecture, and performance-critical code.

Required Skills

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.