Experience Required
10+ years of handson experience in AI/ML engineering, with strong depth in knowledge graphs, unstructured data processing, and generative AI systems.
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 largescale unstructured data into enterprisegrade 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, finetuning pipelines, and graphbased reasoning systems.
This role involves architecting and delivering productiongrade 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 enterprisescale Knowledge Graphs from large volumes of unstructured data (text, documents, logs, PDFs, web data).
Create and evolve ontologies using RDF/OWL, including:
o Entity extraction and linkingo Entity resolution and disambiguationo Probabilistic pattern matchingo 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:o Automated data gap identificationo Knowledge base enrichment and validationo Continuous learning and selfimproving graph pipelines Build workflows that combine LLM reasoning with graph traversal and inference .AI/ML & GenAI Systems Design and implement AI/ML pipelines integrating:o Large Language Models (LLMs)o Small Language Models (SMLs)o Reasoning and taskspecific models Build finetuning pipelines , including:o Dataset generation and curationo Training and finetuning (SFT, PEFT, adapters)o 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:o Semantic inconsistencieso Behavioral anomalieso Data quality and relationship driftData & ML Engineering Build robust data pipelines that ingest, process, enrich, and publish knowledge graph data. Implement scalable ML systems using Python for:o Model developmento Training and tuningo Inference and deployment Technical Skills & ExpertiseCore AI/ML Strong AI/ML engineering background with deep expertise in:o Pythono Model development, training, tuning, and deployment Extensive handson experience with:o Large Language Models (LLMs)o Small Language Models (SMLs)o Generative AI and reasoning modelso Text generation, summarization, and semantic search workflowsKnowledge Graph Technologies Strong experience with:o Neo4j , GraphDBo RDF, OWLo Cypher , SPARQL Proven ability to implement:o Entity linking and resolutiono Semantic searcho Relationship mapping and inferenceGenAI Frameworks & Tooling Experience building GenAI systems using:o LangChain, LangGrapho LlamaIndexo OpenAI / Azure OpenAIo Vector databases such as Pinecone and FAISSMLOps & LLMOps Strong experience in MLOps and LLMOps , including:o MLflow, Azure ML, Datadogo CI/CD automation for ML systemso Observability, logging, and tracingo Model performance monitoring and drift detection Experience deploying and operating AI systems in production environments.Cloud & Scalability Experience building and optimizing AI/ML and graph pipelines either of any on:o Azureo AWSo Google Cloud Platform Strong understanding of distributed systems, scalability, and performance optimization.
