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AI Summary
Build and productionize multi-agent workflows and hybrid retrieval architectures using LLM APIs. Develop evaluation harnesses and observability tools to ensure the reliability and quality of agent behavior in production.
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.
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