Summary
✨ AI‑Generated
Build production AI systems focused on retrieval-augmented generation and agentic workflows. You will design custom retrieval pipelines, implement multi-step agents with memory and tool execution, optimize prompts and context usage, and expose AI capabilities through robust Python APIs.
Highlights
Build sophisticated RAG pipelines and agentic AI systems, optimize context and inference costs, and develop production-grade AI services with modern Python tooling.
Description
Job Title: AI Developer
Location: Brampton, Ontario
Total Experience - 8+ Years
Job Description:
What You Will Do:
• 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
Thanks & Regards
Michael
michael@ampstek.com
