Data Scientist / Graph AI Engineer - Only USC/GC Holder (10+ years of exp)

Overview

Position: Data Scientist / Graph AI Engineer. Open for FTE and contract both. Location: Austin, TX. We are seeking a Data Scientist / Graph AI Engineer with deep expertise in semantic graph analytics, AI-driven anomaly detection, and large language models (LLMs). This role involves designing, implementing, and validating novel methodologies to transform machine log data into ontology-driven semantic graphs that enable clustering, anomaly detection, and downstream analytics. The candidate should be a thinker, builder, and innovator who thrives in customer-centric environments, can invent intellectual property, and can operate at the intersection of data engineering, graph representation learning, and AI/LLM-based methodology creation. Note:

This description reflects the core role and qualifications; other job postings and salary blocks present in the original content have been removed as unrelated boilerplate. Responsibilities

Design, implement, and validate methods to convert machine log data into ontology-driven semantic graphs. Apply graph analytics for clustering, anomaly detection, and downstream insights. Explore and implement AI/LLM-based approaches for data representation, semantic reasoning, or code generation where relevant. Collaborate with stakeholders to translate business requirements into technical solutions. Contribute to IP creation through novel algorithms, patents, or publications where applicable. Required Skills & Experience

Graph Expertise: Strong background in graph databases (Neo4j, TigerGraph), graph processing (NetworkX, DGL, PyTorch Geometric), and ontology modeling (OWL, RDF, Protégé). Machine Learning: Experience with graph embeddings, anomaly detection, clustering, and time-series analysis. AI/LLM Innovation: Hands-on experience applying or extending large language models for data representation, semantic reasoning, or code generation. Programming & Engineering: Proficient in Python, PyTorch/TensorFlow, Spark, and cloud-native pipelines. Research & IP Creation: Track record of innovation (patents, publications, novel algorithms). Communication: Ability to engage stakeholders with clarity, empathy, and influence. Experience with Splunk log data or similar enterprise log platforms. Familiarity with graph-based anomaly detection benchmarks and scalable ML infrastructure. Seniority level

Mid-Senior level Employment type

Contract Full-time (FTE) also considered Job function

Information Technology Industries

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