Principal AI ML Engineer

Onsite in Dallas, TX

Contract W2 only

Experience Required

10+ years of hands on experience in AI/ML engineering, with strong depth in knowledge graphs, unstructured data processing, and generative AI systems.

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Role Summary

We are seeking a highly experienced AI/ML Engineer with a strong foundation in knowledge graph engineering and generative AI to design, build, and scale intelligent data pipelines that transform large scale unstructured data into enterprise grade Knowledge Graphs.

The ideal candidate will have deep experience in ontology modeling, entity resolution, probabilistic pattern matching, and agentic knowledge base enrichment, combined with strong expertise in LLMs/SMLs, fine tuning pipelines, and graph based reasoning systems.

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.

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Key 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:

o Entity extraction and linking

o Entity resolution and disambiguation

o Probabilistic pattern matching

o Ontology alignment across heterogeneous data sources

• Implement semantic modeling for complex domains to support reasoning, discovery, and analytics.

Agentic Knowledge Base Enrichment

• Develop agentic AI systems for:

o Automated data gap identification

o Knowledge base enrichment and validation

o Continuous learning and self improving graph pipelines

• Build workflows that combine LLM reasoning with graph traversal and inference.

AI/ML & GenAI Systems

• Design and implement AI/ML pipelines integrating:

o Large Language Models (LLMs)

o Small Language Models (SMLs)

o Reasoning and task specific models

• Build fine tuning pipelines, including:

o Dataset generation and curation

o Training and fine tuning (SFT, PEFT, adapters)

o Evaluation, benchmarking, and deployment

• Apply prompt engineering, RAG, and hybrid LLM + Knowledge Graph (GraphRAG) techniques for contextual intelligence.

Thank you

Vinod

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