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

Position: Data Scientist / Graph AI Engineer Location: Austin, TX/ Cupertino, CA (Hybrid) Open for FTE and contract both Job Description Overview 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 individual will serve as a technical pioneer, designing, implementing, and validating novel methodologies to transform machine log data into ontology-driven semantic graphs that enable clustering, anomaly detection, and downstream analytics. This role demands a thinker, builder, and innovator who thrives in customer-centric environments, can invent intellectual property, and can navigate the intersection of data engineering, graph representation learning, and AI/LLM-based methodology creation. 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: Proven 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: Advanced skills 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.