Principal / Lead AI ML Engineer Knowledge Graphs & GenAI - Onsite in Dallas TX. Must have 14+ years of hands on experience in AI/ML engineering, LLMs, SMLs, SFT, PEFT, adapters, RAG, and hybrid LLM + Knowledge Graph (GraphRAG) techniques.

Job Title - Principal / Lead AI ML Engineer Knowledge Graphs & GenAI
Location - Onsite in Dallas, TX
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Experience Required
14+ 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.

Anomaly Detection & Analytics
Develop anomaly detection systems on top of knowledge graph data at scale.
Apply graph analytics, embeddings, and ML techniques to detect:
o Semantic inconsistencies
o Behavioral anomalies
o Data quality and relationship drift

Data & ML Engineering
Build robust data pipelines that ingest, process, enrich, and publish knowledge graph data.
Implement scalable ML systems using Python for:
o Model development
o Training and tuning
o Inference and deployment
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Technical Skills & Expertise
Core AI/ML
Strong AI/ML engineering background with deep expertise in:
o Python
o Model development, training, tuning, and deployment
Extensive hands on experience with:
o Large Language Models (LLMs)
o Small Language Models (SMLs)
o Generative AI and reasoning models
o Text generation, summarization, and semantic search workflows
Knowledge Graph Technologies
Strong experience with:
o Neo4j, GraphDB
o RDF, OWL
o Cypher, SPARQL
Proven ability to implement:
o Entity linking and resolution
o Semantic search
o Relationship mapping and inference

GenAI Frameworks & Tooling
Experience building GenAI systems using:
o LangChain, LangGraph
o LlamaIndex
o OpenAI / Azure OpenAI
o Vector databases such as Pinecone and FAISS

MLOps & LLMOps
Strong experience in MLOps and LLMOps, including:
o MLflow, Azure ML, Datadog
o CI/CD automation for ML systems
o Observability, logging, and tracing
o Model performance monitoring and drift detection
Experience deploying and operating AI systems in production environments.

Cloud & Scalability
Experience building and optimizing AI/ML and graph pipelines either of any on:
o Azure
o AWS
o Google Cloud Platform
Strong understanding of distributed systems, scalability, and performance optimization.
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Client is looking for candidates who have experience in building:
Ontology from large scale data (requires experience in entity resolution, probabilistic pattern matching)
Agentic knowledge-base enrichment (automated data gap identification, and data enrichment)
Anomaly detection on top of knowledge graph data at scale
Fine tuning pipeline (including dataset generation, tuning, evaluation, deployment) for small language models and reasoning models