Job Requirements 2.1 AI Engineers (2 positions) The AI engineers will design, build, and iterate on AI agent capabilities within an agentic orchestration framework. Work will span: · Agent orchestration and workflow execution - Implementing multi-step agent pipelines with human-in-the-loop approval gates, state persistence, and resumable execution · Document intelligence - Building extraction and classification capabilities for engineering documents (single-line diagrams, bills of material, mechanical drawings, site profiles, tariff schedules) using LLM-based pipelines · Knowledge graph construction and validation - AI-driven generation, enrichment, and deterministic validation of RDF/OWL knowledge graphs representing physical assets and their relationships · Conversational AI - Extending a chat-based interface that allows operators to query operational data, trigger agent workflows, and review AI-generated recommendations · Device and telemetry classification - Agents that identify and classify new devices and telemetry streams appearing on the network, mapping them to canonical ontology types · Observability and evaluation - Instrumenting agents with structured logging, tracing, and quality scoring to support iterative prompt engineering and reliability improvements Required skills: - Strong Python development (3+ years production experience) - Hands-on experience building LLM-powered applications (prompt engineering, RAG, tool use / function calling, structured output) - Experience with at least one agent framework (LangGraph, Semantic Kernel, AutoGen, CrewAI, or equivalent) - Familiarity with document extraction pipelines (OCR, PDF parsing, layout-aware models) - Working knowledge of RDF, OWL, SPARQL, or similar knowledge representation technologies - Experience with Azure AI services (Azure OpenAI, Azure AI Document Intelligence, or Azure AI Foundry) Preferred skills: - Experience with Model Context Protocol (MCP) tool servers - Background in energy, industrial IoT, or SCADA systems - Familiarity with Sparkplug B or MQTT protocol stacks - Experience with evaluation frameworks for LLM applications (e.g., promptfoo, ragas, or custom harnesses)