Sans
Lead AI/ML Engineer
Charlotte, North Carolina, USA · Contract
Sponsorship not specifiedDetected 75 days ago
PythonDistributed SystemsVector DatabasesAWSGCPAzureCloud PlatformsCI/CDDatadogMachine LearningData EngineeringNLPLLMsRAGAgentic AILLMOpsMLOpsDetection Engineering
About the role
- This role involves architecting and delivering production grade AI systems that integrate LLMs with knowledge graphs, enabling contextual reasoning, anomaly detection, and intelligent automation at scale.
Responsibilities
- Knowledge Graph & Ontology Engineering Design, build, and maintain enterprise scale Knowledge Graphs from large volumes of unstructured data (text, documents, logs, PDFs, web data).
- Create and evolve ontologies using RDF/OWL, including: Entity extraction and linking Entity resolution and disambiguation Probabilistic pattern matching Ontology alignment across heterogeneous data sources Implement semantic modeling for complex domains to support reasoning, discovery, and analytics.
- Anomaly Detection & Analytics Develop anomaly detection systems on top of knowledge graph data at scale.
- Apply graph analytics, embeddings, and ML techniques to detect: Semantic inconsistencies Behavioral anomalies Data quality and relationship drift Data & ML Engineering Build robust data pipelines that ingest, process, enrich, and publish knowledge graph data.
- Semantic inconsistencies Behavioral anomalies Data quality and relationship drift Data & ML Engineering Build robust data pipelines that ingest, process, enrich, and publish knowledge graph data.
Requirements
- Cloud & Scalability Experience building and optimizing AI/ML and graph pipelines either of any on: Azure AWS Google Cloud Platform Strong understanding of distributed systems, scalability, and performance optimization.
Skills
- Model development Training and tuning Inference and deployment Technical Skills &
- Lang Chain, Lang Graph Llama Index OpenAI / Azure OpenAI Vector databases such as Pinecone and
- FAISS MLOps & LLMOps Strong experience in MLOps and LLMOps, including:
- Cloud & Scalability
- Azure AWS Google Cloud Platform Strong understanding of distributed systems, scalability, and performance optimization.
Benefits
- Automated data gap identification Knowledge base enrichment and validation Continuous learning and selfimproving graph pipelines Build workflows that combine LLM reasoning with graph traversal and inference.
This listing is sourced directly from Sans's careers page and normalized into a canonical job model.