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

📌 Delivery Lead- AI and Ontology Engineer (Bengaluru)
🏢 Acuity Analytics
📍 Bengaluru