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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 Role

You 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 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.