Applied AI Engineer

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Symmetry is building an intelligence layer to bring context, continuity, and observability to AI-powered work. As an Applied AI Engineer, you will own 0→1 systems for knowledge extraction, mapping, and retrieval, turning experiments into production LLM systems. You will work on retrieval infrastructure, agentic systems, and evaluation pipelines to power real user experiences.

• Build core AI systems to extract and organize knowledge from AI conversations into structured context.
• Develop retrieval and memory infrastructure using GraphRAG and vector search for low-latency context delivery.
• Design agentic systems that reason across stored context for retrieval, synthesis, and evaluation.
• Create prompt and orchestration frameworks connecting multiple models, tools, and data sources.
• Implement evaluation and telemetry systems to benchmark retrieval quality, latency, and intelligence performance.
• Prototype quickly to explore new product directions and validate user-facing capabilities.

• Strong Python engineering fundamentals for building performant, maintainable systems.
• Deep understanding of retrieval architectures: embeddings, vector databases, hybrid/graph search, caching.
• Experience with LLM orchestration frameworks like LangChain, LlamaIndex, or custom agent systems.
• Proven ability to build and tune LLM-based agents for reasoning, synthesis, or evaluation.
• Familiarity with prompt engineering and multi-step reasoning.
• Exposure to fine-tuning or adapter training (LoRA, PEFT) and integration into retrieval pipelines.
• Ability to work end-to-end from backend (FastAPI, Node) to quick front-end demos.

• Experience with front-end frameworks for rapid prototyping.
• Familiarity with telemetry and observability tools.
• Prior startup experience in early-stage environments.