Lead AI/ML Engineer - Knowledge Graphs & GenAI
SANS Consulting Services, Inc., Irving, TX, United States
Title: Lead AI‐ML Engineer – Knowledge Graphs & GenAI
Location - Irving, TX (Hybrid – 3 days a week)
Duration: Long Term
The following information aims to provide potential candidates with a better understanding of the requirements for this role.
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.
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:
Entity extraction and linking
Entity resolution and disambiguation
Probabilistic pattern matching
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:
Automated data gap identification
Knowledge base enrichment and validation
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:
Large Language Models (LLMs)
Small Language Models (SMLs)
Reasoning and task‐specific models
Build fine‐tuning pipelines , including:
Dataset generation and curation
Training and fine‐tuning (SFT, PEFT, adapters)
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:
Semantic inconsistencies
Behavioral anomalies
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:
Model development
Training and tuning
Inference and deployment
Technical Skills & Expertise
Core AI/ML
Strong AI/ML engineering background with deep expertise in:
Python
Model development, training, tuning, and deployment
Extensive hands‐on experience with:
Large Language Models (LLMs)
Small Language Models (SMLs)
Generative AI and reasoning models
Text generation, summarization, and semantic search workflows
Knowledge Graph Technologies
Strong experience with:
Neo4j , GraphDB
RDF, OWL
Cypher , SPARQL
Proven ability to implement:
Entity linking and resolution
Semantic search
Relationship mapping and inference
GenAI Frameworks & Tooling
Experience building GenAI systems using:
LangChain, LangGraph
LlamaIndex
OpenAI / Azure OpenAI
Vector databases such as Pinecone and FAISS
MLOps & LLMOps
Strong experience in MLOps and LLMOps , including:
MLflow, Azure ML, Datadog
CI/CD automation for ML systems
Observability, logging, and tracing
Model performance monitoring and drift detection
Experience deploying and operating AI systems in production environments. xywuqvp
Cloud & Scalability
Experience building and optimizing AI/ML and graph pipelines either of any on:
Azure
AWS
GCP
Strong understanding of distributed systems, scalability, and performance optimization.
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
Regards ,
Priyank Varma,
Lead Recruiter
Email:
Linkedin: Thornall St. Suite 375 Edison, NJ 08837a
Direct: (XXX) XXX-XXXX
Location - Irving, TX (Hybrid – 3 days a week)
Duration: Long Term
The following information aims to provide potential candidates with a better understanding of the requirements for this role.
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.
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:
Entity extraction and linking
Entity resolution and disambiguation
Probabilistic pattern matching
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:
Automated data gap identification
Knowledge base enrichment and validation
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:
Large Language Models (LLMs)
Small Language Models (SMLs)
Reasoning and task‐specific models
Build fine‐tuning pipelines , including:
Dataset generation and curation
Training and fine‐tuning (SFT, PEFT, adapters)
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:
Semantic inconsistencies
Behavioral anomalies
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:
Model development
Training and tuning
Inference and deployment
Technical Skills & Expertise
Core AI/ML
Strong AI/ML engineering background with deep expertise in:
Python
Model development, training, tuning, and deployment
Extensive hands‐on experience with:
Large Language Models (LLMs)
Small Language Models (SMLs)
Generative AI and reasoning models
Text generation, summarization, and semantic search workflows
Knowledge Graph Technologies
Strong experience with:
Neo4j , GraphDB
RDF, OWL
Cypher , SPARQL
Proven ability to implement:
Entity linking and resolution
Semantic search
Relationship mapping and inference
GenAI Frameworks & Tooling
Experience building GenAI systems using:
LangChain, LangGraph
LlamaIndex
OpenAI / Azure OpenAI
Vector databases such as Pinecone and FAISS
MLOps & LLMOps
Strong experience in MLOps and LLMOps , including:
MLflow, Azure ML, Datadog
CI/CD automation for ML systems
Observability, logging, and tracing
Model performance monitoring and drift detection
Experience deploying and operating AI systems in production environments. xywuqvp
Cloud & Scalability
Experience building and optimizing AI/ML and graph pipelines either of any on:
Azure
AWS
GCP
Strong understanding of distributed systems, scalability, and performance optimization.
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
Regards ,
Priyank Varma,
Lead Recruiter
Email:
Linkedin: Thornall St. Suite 375 Edison, NJ 08837a
Direct: (XXX) XXX-XXXX