A hands-on builder who writes the native code powering 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 RoleYou will implement the mechanical core of 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.Skillset RequirementsNative 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: Robust fundamentals, async programming, concurrency, and performance tuning.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 debuggingObservability: Instrumenting tracing, logging, and token/latency metrics for agents and RAG components.#J-18808-Ljbffr

📌 Ai Developer (Winnipeg)
🏢 apptoza
📍 Winnipeg