Principal / Lead AI ML Engineer – Knowledge Graphs & Genai

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.

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.