Delivery Lead- AI and Ontology Engineer

Job Purpose

We are seeking a highly skilled AI & Ontology/Knowledge Graph Engineer to design, build, and scale enterprise-grade AI solutions powered by semantic technologies and knowledge graphs. The ideal candidate will possess a unique blend of AI engineering, data engineering, ontology modeling, and knowledge graph expertise, enabling the development of intelligent, agent-driven systems that transform complex enterprise data into actionable knowledge.

This role requires both strategic architecture capabilities and hands-on implementation experience, with a focus on building AI-powered, no-code/low-code data processing pipelines and enterprise knowledge ecosystems.

Key Responsibilities

AI Engineering & Agentic Systems

• Design and develop AI-powered solutions leveraging Large Language Models (LLMs), Generative AI, Agentic AI, and Retrieval-Augmented Generation (RAG) frameworks.
• Build intelligent agent-managed data pipelines that enable business users to create, manage, and monitor data workflows without writing code.
• Develop AI orchestration frameworks integrating enterprise systems, APIs, databases, and knowledge repositories.
• Implement prompt engineering, tool calling, workflow automation, and autonomous agent architectures.
• Optimize AI model performance, scalability, governance, and operationalization in enterprise environments.
• Develop evaluation frameworks and guardrails for AI applications.

Ontology & Knowledge Graph Engineering

• Design and develop enterprise ontologies, taxonomies, semantic models, and knowledge graphs.
• Define domain concepts, relationships, metadata standards, and semantic frameworks across multiple business domains.
• Architect and implement knowledge graph solutions using graph databases and semantic web technologies.
• Build semantic layers that connect structured, unstructured, and semi-structured enterprise data.
• Develop ontology governance processes and lifecycle management frameworks.
• Enable semantic search, entity resolution, relationship discovery, and contextual reasoning capabilities.

Data Engineering & Integration

• Build scalable data ingestion and transformation pipelines across enterprise platforms.
• Integrate knowledge graphs with data lakes, data warehouses, AI platforms, and enterprise applications.
• Develop metadata-driven architectures to improve discoverability, governance, and lineage.
• Implement data quality, enrichment, and semantic tagging frameworks.

Architecture & Leadership

• Define enterprise architecture for AI and Knowledge Graph platforms.
• Collaborate with business stakeholders, data stewards, architects, and product teams to translate business requirements into semantic and AI solutions.
• Provide technical leadership and best practices for ontology design, graph modeling, and AI engineering.
• Mentor team members and contribute to capability development within the organization.

Key competencies

Experience

• 8+ years of experience in AI Engineering, Data Engineering, Knowledge Management, or Semantic Technologies.
• Proven experience designing and implementing enterprise-scale Knowledge Graph solutions.
• Hands-on experience building production-grade AI applications and data pipelines.
• Experience architecting and delivering large-scale enterprise platforms.

Technical Skills

AI & Generative AI

• Large Language Models (OpenAI, Claude, Llama, Gemini, Azure OpenAI)
• Agentic AI Frameworks
• LangGraph
• LangChain
• CrewAI
• Semantic Kernel
• AutoGen
• Retrieval-Augmented Generation (RAG)
• Vector Databases
• Prompt Engineering
• AI Evaluation Frameworks

Ontology & Knowledge Graph

• Ontology Design & Development
• Taxonomy Management
• RDF, RDFS, OWL
• SPARQL
• Knowledge Graph Modeling
• Semantic Web Technologies
• Entity Resolution and Semantic Reasoning

Graph Technologies

Experience with one or more:

• Neo4j
• Stardog
• GraphDB
• Amazon Neptune
• TigerGraph
• Azure Cosmos DB (Gremlin)
• Ontotext Platform

Data Engineering

• Python (mandatory)
• PySpark / Spark
• REST APIs
• ETL/ELT Development
• Data Modeling
• SQL and NoSQL Databases
• Data Lake Architectures

Cloud Platforms

Experience with one or more:

• Azure
• AWS
• Google Cloud Platform