Principal / Lead AI ML Engineer – Knowledge Graphs & GenA

TechDigital Group; Dallas (TX); On-site; USD 130,000 - 160,000; Full time

Job summary

TechDigital Group is searching for a highly experienced AI/ML Engineer to design and build scalable data pipelines that transform unstructured data into knowledge graphs. Applicants should have over 10 years of relevant experience and proficiency in AI/ML, with a focus on ontology modeling and generative AI systems.

The role involves architecting production-grade AI systems using Large and Small Language Models, and requires strong technical skills in tools like Neo4j and Azure ML. The position is based in Dallas, Texas, and offers opportunities to innovate and lead in the domain of AI systems.

Qualifications

• 10+ years of hands-on experience in AI/ML engineering.
• Deep expertise in knowledge graphs and generative AI.
• Experience in ontology modeling and entity resolution.

Responsibilities

• Design and maintain enterprise-scale Knowledge Graphs.
• Develop agentic AI systems for automated data gap identification.
• Build AI/ML pipelines integrating Large and Small Language Models.
• Implement anomaly detection systems on knowledge graph data.

Skills

AI/ML Engineering

Knowledge Graph Engineering

Generative AI

Python

Entity Resolution

Probabilistic Pattern Matching

Anomaly Detection

Education

10+ years of hands-on experience

Tools

Neo4j

GraphDB

MLflow

Azure ML

Job description

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.

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.

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:
  1. Entity extraction and linking
  2. Entity resolution and disambiguation
  3. Probabilistic pattern matching
  4. 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:
  1. Automated data gap identification
  2. Knowledge base enrichment and validation
  3. 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:
  1. Large Language Models (LLMs)
  2. Small Language Models (SMLs)
  3. Reasoning and task specific models
• Build fine tuning pipelines, including:
  1. Dataset generation and curation
  2. Training and fine‑tuning (SFT, PEFT, adapters)
  3. 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:
  1. Semantic inconsistencies
  2. Behavioral anomalies
  3. 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:
  1. Model development
  2. Training and tuning
  3. Inference and deployment

Technical Skills & Expertise

Core AI/ML

• Strong AI/ML engineering background with deep expertise in:
  1. Python
  2. Model development, training, tuning, and deployment
• Extensive hands‑on experience with:
  1. Large Language Models (LLMs)
  2. Small Language Models (SMLs)
  3. Generative AI and reasoning models
  4. Text generation, summarization, and semantic search workflows

Knowledge Graph Technologies

• Strong experience with:
  1. Neo4j, GraphDB
  2. RDF, OWL
  3. Cypher, SPARQL
• Proven ability to implement:
  1. Entity linking and resolution
  2. Semantic search
  3. Relationship mapping and inference

GenAI Frameworks & Tooling

• Experience building GenAI systems using:
  1. LangChain, LangGraph
  2. LlamaIndex
  3. OpenAI / Azure OpenAI
  4. Vector databases such as Pinecone and FAISS

MLOps & LLMOps

• Strong experience in MLOps and LLMOps, including:
  1. MLflow, Azure ML, Datadog
  2. CI/CD automation for ML systems
  3. Observability, logging, and tracing
  4. 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 in any on:
  1. Azure
  2. AWS
  3. GCP
• Strong understanding of distributed systems, scalability, and performance optimization.

Client Requirements

• 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