Description

This job posting is for an existing, active vacancy and we are looking to hire AI Developer at Brampton, ON (Onsite), immediately who has strong experience in Coding, RAG Pipelines, Python.

Role Overview

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.

About the Role

You will implement the mechanical core of our AI features: chunking logic, embedding flows, retrieval algorithms, agent state machines, and prompt-construction engines. You will 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.

Required 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.

Skillset Requirements

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.

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