About this role

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. Originally posted on Himalayas

Source listing: himalayas_finland_p2