Urgent Contract Hiring Principal / Lead AI ML Engineer – Graphs & GenAI NO H1B Texas Locals Only
Aditya Jain
Hi,
Please share relevant Profiles Only NO H1B NO GC GCEAD USC OF 10 YEARS [GENUINE PROFILES ONLY] LOCALS ONLY H4 TN OPT IS ALSO FINE BUT NO H1B
Principal / Lead AI ML Engineer – Knowledge Graphs & GenAI
- Job Title: Principal / Lead AI ML Engineer – Knowledge Graphs & GenAI
- Location: Dallas, TX (Onsite)
- Experience Level: 10+ years hands-on AI/ML engineering
Required Skills & Qualifications
- 10+ years of hands-on experience in AI/ML engineering, with strong depth in knowledge graphs, unstructured data processing, and generative AI systems
- 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
- Strong experience with Neo4j, GraphDB, RDF, OWL, Cypher, SPARQL
- Proven ability to implement entity linking and resolution, semantic search, relationship mapping, and inference
- Experience building GenAI systems using LangChain, LangGraph, LlamaIndex, OpenAI / Azure OpenAI, and vector databases (Pinecone, FAISS)
- Strong experience in MLOps and LLMOps including MLflow, Azure ML, Datadog, CI/CD automation, observability, logging, tracing, and model performance monitoring
- Experience building and optimizing AI/ML and graph pipelines on Azure, AWS, or GCP
- Strong understanding of distributed systems, scalability, and performance optimization
Role Overview
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.
Technical Core & 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, and 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, and 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 LLMs, SMLs, and reasoning models
- Build fine tuning pipelines including dataset generation and curation, training and fine tuning (SFT, PEFT, adapters), and 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, and 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, and inference and deployment
Client Focus Areas
The 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
Thanks,
_______________________________________
Aditya Jain | New York Technology Partners
120 Wood Avenue S | Suite 504 | Iselin NJ 08830
Email: adity...@nytpcorp.com
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