AI Engineer
Job Details
About the Company
With operational hubs scattered across Europe, Asia, and LATAM, and its headquarters situated in San Francisco, US, the company boasts a workforce of over 1,000 adept professionals. Spanning across more than 20 countries, ALLSTARSIT offers a diverse range of skilled employees across various verticals, including AI, cybersecurity, healthcare, fintech, telecom, media, and so on.
About the Project
Our client is an applied AI lab building semantic AI that can actually think with you — grounded in formal knowledge representations and sound reasoning. Their mission is to create trusted agents that reason over deep, structured knowledge: texts, commentaries, arguments, and traditions.
We’re helping them look 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.
Specialization
Headquarters
Years on the market
Team size and structure
Current technology stack
Required skills:
- 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.
- 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.
Scope of work:
- 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.