AI Engineer

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.