Principal / Lead AI ML Engineer – Knowledge Graphs & Gena

AI/ML Engineer

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:
• 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
• 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.