Principal / Lead AI/ML Engineer

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

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

Featured in CNBC, Digital Journal, Fox News, and CIO Review, Genius Business Solutions Inc. (GBSI) is a globally recognized IT services leader with 20+ years of experience serving Fortune 500 organizations. Our teams deliver cutting-edge solutions across industries such as Healthcare, Life Sciences, Automotive, Manufacturing, and Consumer Goods helping clients transform business processes through innovation and technology.