Senior AI Engineer — Agentic Systems & Knowledge Graphs
Premium Ad IT/Computer - Other IT/Computer Middelburg FULL TIME Job Summary A leading internet provider is looking for a Senior AI Engineer to join their team in Middelburg, Mpumalanga. The Mission: We are building a sovereign, company-wide AI fabric. This system ingests every document, ticket, transaction, and data point across our portfolio (Veralogix, Bioniq, Innovata, Treadstone, Aiguille) into a unified retrieval, property-graph, and agentic layer. It provides natural-language, grounded, and traceable insights in real-time. You will own the end-to-end architecture as part of a small, high-impact engineering team, focusing on on-prem-first deployment and seamless integration with our spatio-temporal platform. Responsibilities: 1. The Retrieval Spine * Design and implement a chunking strategy for heterogeneous content: PDFs, scanned docs, transcripts, tables, code, and ERP records. * Engineer hybrid search (BM25 + dense + late-interaction), reranking, and query routing. * Architect and operate the vector database infrastructure (pgvector + Qdrant or equivalent). 2. The Corporate Knowledge Graph * Design and implement the ontology for the corporate knowledge graph (Memgraph, Neo4j, or KuzuDB). * Execute entity resolution across systems like Odoo, Aleph, Hoover, Elasticsearch, Nextcloud, and telemetry. * Build GraphRAG patterns that solve complex, multi-hop queries more effectively than naïve vector retrieval. 3. The MCP Server Fleet * Author and manage a fleet of MCP servers to expose internal systems (Odoo, Hoover, Aleph, Memgraph, Elasticsearch, etc.) to the agent layer. * Implement robust scoping, authentication, auditing, and rate limiting. 4. Agentic Orchestration * Build and deploy multi-agent systems using frameworks like LangGraph, Pydantic AI, DSPy, or BAML. * Implement planner/researcher/executor/verifier patterns with structured outputs, error recovery, and long-running stateful workflows (e.g., using Temporal). 5. Eval, Observability & LLM-Ops * Build an evaluation harness from scratch with golden sets and RAGAS-style metrics. * Establish a rigorous "LLM-as-judge" methodology to block deploys on regression. * Set up observability (Langfuse/Phoenix), cost/latency dashboards, and a version-controlled prompt management system. Requirements:…
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