AI/ML Engineer

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
• Experience deploying and operating AI systems in production environments.

Cloud & Scalability

• Experience building and optimizing AI/ML and graph pipelines either of any on:
  • Azure
  • AWS
  • Google Cloud Platform
• 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