Job VC

We’re looking for an AI Engineer to build the LLM layer of our platform: multi-agent workflows, hybrid retrieval over knowledge graphs and vector indexes, inference integration, and evaluation.
You’ll sit between research and platform — taking agent architectures from prototype to production and making them measurably reliable.

Your role

Build and productionize multi-agent workflows on Anthropic/OpenAI APIs: orchestration, tool use, structured outputs, guardrails.
Design hybrid retrieval architectures that combine knowledge graphs, vector search, and ranking into a single coherent context layer.
Build evaluation harnesses and observability for agent behavior — quality, latency, cost — and use them to drive iteration.
Integrate LLM inference, retrieval, and reasoning services into production backends.
Work with researchers and domain experts to turn neuro-symbolic prototypes into robust product features.

What you’ll need

4+ years of software engineering experience (backend or ML), including production systems in Python.
Hands-on experience building LLM systems beyond demos: agents and tool use, RAG, or evaluation pipelines.
Real workflow experience with the OpenAI/Anthropic APIs (or comparable).
Solid engineering fundamentals: API design, services, testing, deployment.

Nice to have
Structured knowledge representations: ontologies, knowledge graphs, SPARQL, or graph databases (e.g., Neo4j).
Vector databases and hybrid retrieval architectures.
Kubernetes and cloud-native deployment.
Model serving (e.g., vLLM), fine-tuning, or evaluation frameworks.
Go and/or Scala.