Summary
A hands-on artificial intelligence engineering role building intelligent applications through retrieval pipelines, autonomous workflows, context optimization, and scalable software components.
Highlights
Hands-on AI engineering role focused on building advanced retrieval and agent systems with opportunities to optimize enterprise-scale solutions.
Description
Job Title: AI Developer
Job location:: Toronto, Brampton, Canada
A hands-on builder who writes the native code powering our RAG pipelines, agentic workflows, and
context-engineering systems.
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.
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 GECX.
• Experience with structured extraction using JSON schemas, Pydantic, and advanced prompting
AI Developer - Skillset Requirements
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, and Regards
Anil Kumar | Technical Recruiter
Email: anil.k@ampstek.com
Desk: 6095361083
www.ampstek.com
