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Senior AI Engineer
AiRo Digital Labs
3 hours ago
Full-time
On-site
Kentucky, United States
The technical owner of the Core Intelligence layer — the platform’s differentiator. You design and build the Signal Engine, Action Engine, Librarian Agent and Ontology Graph, turning signals into coordinated, agent-driven action.
Key Responsibilities
Design and implement agentic workflows with LangGraph and Azure AI Foundry that power the Signal and Action Engines. Build the Librarian Agent — retrieval-augmented reasoning over the Ontology Graph and Graph Database. Model and evolve the Ontology Graph; implement graph and vector retrieval strategies. Integrate agents with tools and other services via MCP / A2A and webhooks. Establish evaluation, guardrails, observability and cost controls for LLM/agent behavior in production. Partner with the Data Engineer on the data and features that feed the intelligence layer. Must-have Skills & Technologies
Strong Python and production ML/AI engineering. Agentic frameworks — LangGraph (or LangChain, LlamaIndex, Semantic Kernel) — and LLM orchestration patterns. Azure AI Foundry or equivalent (Azure OpenAI, Bedrock, Vertex) model deployment and management. RAG, embeddings, vector search, and graph-based retrieval; hands-on with a Graph Database (Neo4j, Cosmos DB Gremlin or similar). Prompt engineering, structured output / tool-calling, and LLM evaluation frameworks. Prior Experience
5+ years in ML/AI engineering, including production LLM or agent systems (not only notebooks/POCs). Experience taking an agentic or RAG system from prototype to reliable, monitored production. Nice to Have
Ontology / knowledge-graph modelling; MCP / A2A protocol experience. MLOps / LLMOps: eval harnesses, prompt versioning, drift and cost monitoring.
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Design and implement agentic workflows with LangGraph and Azure AI Foundry that power the Signal and Action Engines. Build the Librarian Agent — retrieval-augmented reasoning over the Ontology Graph and Graph Database. Model and evolve the Ontology Graph; implement graph and vector retrieval strategies. Integrate agents with tools and other services via MCP / A2A and webhooks. Establish evaluation, guardrails, observability and cost controls for LLM/agent behavior in production. Partner with the Data Engineer on the data and features that feed the intelligence layer. Must-have Skills & Technologies
Strong Python and production ML/AI engineering. Agentic frameworks — LangGraph (or LangChain, LlamaIndex, Semantic Kernel) — and LLM orchestration patterns. Azure AI Foundry or equivalent (Azure OpenAI, Bedrock, Vertex) model deployment and management. RAG, embeddings, vector search, and graph-based retrieval; hands-on with a Graph Database (Neo4j, Cosmos DB Gremlin or similar). Prompt engineering, structured output / tool-calling, and LLM evaluation frameworks. Prior Experience
5+ years in ML/AI engineering, including production LLM or agent systems (not only notebooks/POCs). Experience taking an agentic or RAG system from prototype to reliable, monitored production. Nice to Have
Ontology / knowledge-graph modelling; MCP / A2A protocol experience. MLOps / LLMOps: eval harnesses, prompt versioning, drift and cost monitoring.
#J-18808-Ljbffr