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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Principal / Lead AI/ML Engineer - **Company:** Genius Business Solutions Inc - **Location:** Dallas, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Data Analysis, Microsoft Azure, Cloud Computing, Continuous Integration, Information Engineering, Data Files, Distributed Systems, Graph Database, Monitoring of Systems, Python (Programming Language), Machine Learning, Language Modeling, Neo4j, Named Entity Recognition, Pattern Recognition, Performance Tuning, Azure Machine Learning, Search Technologies, Software Deployment, SPARQL, Unstructured Data, Datadog, Google Cloud, Large Language Models, Prompt Engineering, Model Validation, Generative AI, AI Platforms, Machine Learning Operations, Data Pipelines, Automation Anywhere - **Published:** May 20, 2026 - **Apply:** https://www.dice.com/job-detail/31539ef4-11d0-4568-8961-d7e405315c03 ## About the Role Core AI/ML: * 14+ years of hands-on AI/ML engineering experience * Strong expertise in: * Python * Model development and deployment * ML training and optimization Extensive experience with: * Large Language Models (LLMs) * Small Language Models (SMLs) * Generative AI systems * Reasoning models * Semantic search and summarization workflows Knowledge Graph Technologies: * Hands-on expertise with: * Neo4j * GraphDB * RDF / OWL * Cypher * SPARQL Strong experience implementing: * Entity linking and resolution * Semantic search * Relationship inference * Ontology modeling GenAI Frameworks & Tooling: * Experience with: * LangChain * LangGraph * LlamaIndex * OpenAI / Azure OpenAI * Vector databases such as Pinecone and FAISS Strong understanding of GraphRAG and hybrid graph + LLM systems MLOps / LLMOps: * Experience with: * MLflow * Azure ML * Datadog * CI/CD for AI systems * Observability and tracing * Model monitoring and drift detection Experience deploying enterprise-grade AI platforms into production Cloud & Scalability: * Strong experience with cloud platforms: * Azure * AWS * Google Cloud Platform Understanding of: * Distributed systems * Scalable AI architectures * Performance optimization * High-throughput data pipelines Preferred Experience: Client is specifically looking for candidates with proven experience building: * Ontology systems from large-scale unstructured data * Entity resolution and probabilistic pattern matching systems * Agentic knowledge-base enrichment platforms * Automated data gap identification and enrichment workflows * Large-scale anomaly detection systems on top of graph data * Fine-tuning pipelines for reasoning models and SMLs including: * Dataset generation * Tuning * Evaluation * Production deployment ## Description We are seeking a highly experienced Principal / Lead AI/ML Engineer with deep expertise in Knowledge Graphs, Generative AI, and enterprise-scale AI systems. The ideal candidate will lead the architecture, development, and deployment of intelligent data platforms that transform massive volumes of unstructured enterprise data into scalable Knowledge Graphs integrated with advanced LLM-driven reasoning systems. This role requires strong hands-on expertise in ontology engineering, entity resolution, probabilistic pattern matching, graph-based reasoning, and GenAI/LLM fine-tuning pipelines. The candidate will work on cutting-edge AI initiatives involving GraphRAG, agentic AI systems, anomaly detection, and intelligent automation at scale., Knowledge Graph & Ontology Engineering * Design, develop, and maintain enterprise-scale Knowledge Graphs using structured and unstructured data sources including documents, PDFs, logs, text, and web data * Build and evolve ontologies using RDF/OWL standards * Implement: * Entity extraction and entity linking * Entity resolution and disambiguation * Probabilistic pattern matching * Ontology alignment across heterogeneous datasets * Develop semantic models supporting reasoning, analytics, and contextual intelligence * Design graph schemas, inference workflows, and relationship mapping systems Agentic Knowledge Base Enrichment * Develop agentic AI systems for: * Automated data gap identification * Knowledge graph enrichment and validation * Self-improving graph learning pipelines * Build AI workflows combining LLM reasoning with graph traversal and semantic inference * Create autonomous enrichment pipelines for continuous knowledge evolution AI/ML & Generative AI Systems * Design and implement AI/ML pipelines leveraging: * Large Language Models (LLMs) * Small Language Models (SMLs) * Reasoning and task-specific AI models * Build and optimize fine-tuning pipelines including: * Dataset generation and curation SFT, PEFT, LoRA, and adapter-based tuning Model evaluation, benchmarking, and deployment * Implement: * Prompt engineering * Retrieval-Augmented Generation (RAG) * GraphRAG architectures * Semantic search and contextual intelligence systems Anomaly Detection & Graph Analytics * Build anomaly detection systems on top of large-scale knowledge graph datasets * Apply graph embeddings, graph analytics, and ML models to detect: * Semantic inconsistencies * Behavioral anomalies * Data quality issues * Relationship drift and graph integrity problems Data Engineering & MLOps: * Build scalable data pipelines for ingesting, enriching, and publishing graph data * Develop production-grade ML systems for: * Training * Tuning * Inference * Deployment Implement robust MLOps and LLMOps frameworks including monitoring, observability, CI/CD, and drift detection ## Related Videos - [How E.On productionizes its AI model & Implementation of Secure Generative AI.](https://www.wearedevelopers.com/videos/623-how-e-on-productionizes-its-ai-model-implementation-of-secure-generative-ai) - [Putting the Graph In GraphQL With The Neo4j GraphQL Library](https://www.wearedevelopers.com/videos/257-putting-the-graph-in-graphql-with-the-neo4j-graphql-library) - [The OpenTelemetry mistakes I keep seeing (and how to stop making them)](https://www.wearedevelopers.com/videos/100158-the-opentelemetry-mistakes-i-keep-seeing-and-how-to-stop-making-them) - [Beyond GPT: Building Unified GenAI Platforms for the Enterprise of Tomorrow](https://www.wearedevelopers.com/videos/1525-beyond-gpt-building-unified-genai-platforms-for-the-enterprise-of-tomorrow) - [Cyber Sleuth: Finding Hidden Connections in Cyber Data](https://www.wearedevelopers.com/videos/893-cyber-sleuth-finding-hidden-connections-in-cyber-data) - [Accelerating Authentication Architecture: Taking Passwordless to the Next Level](https://www.wearedevelopers.com/videos/733-accelerating-authentication-architecture-taking-passwordless-to-the-next-level) ## Related Articles - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Got AI ideas but no money? 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