Applied AI Engineer

If you’re excited by turning product ideas into working LLM systems and iterating through research, data, and prototypes to find what works, you’ll thrive here.

What You’ll Build

Core AI systems that extract and organize knowledge from AI conversations into structured, reusable context.

Retrieval and memory infrastructure (GraphRAG + vector search) that delivers precise, low-latency context to user workflows.

Agentic systems that reason across stored context—handling retrieval, synthesis, and evaluation tasks autonomously.

Prompt and orchestration frameworks that connect multiple models, tools, and data sources into end-to-end reasoning pipelines.

Evaluation and telemetry systems to benchmark retrieval quality, latency, and overall intelligence performance.

Fast prototypes to explore new product directions and validate user-facing capabilities.

Skills We’re Looking For

Strong Python engineering fundamentals—skilled in building performant, maintainable systems and services that connect data, models, and APIs.

Deep understanding of retrieval architectures—embeddings, vector databases, hybrid or graph-based search, and caching strategies.

Experience with LLM orchestration frameworks like LangChain, LlamaIndex, or custom-built agent systems.

Proven ability to build and tune LLM-based agents for reasoning, synthesis, or evaluation tasks.

Familiarity with prompt engineering and multi-step reasoning—designing structured flows that balance quality, latency, and cost.

Exposure to fine-tuning or adapter training (LoRA, PEFT) and how to integrate tuned models into retrieval pipelines.

Ability to work end-to-end—backend (FastAPI, Node) to quick front-end demos or dashboards for testing and iteration.

Comfort operating in open-ended problem spaces, defining your own experiments, and driving them to working outcomes.

If you thrive on autonomy, clarity, and collaboration and want to build the connective tissue between humans and AI systems, Symmetry is where you’ll do your best work.