BENGALURU · FULLTIME
Delivery Lead- AI and Ontology Engineer
Moody's Analytics
Bengaluru · onsite · Posted 3d ago
Sourced from
Undisclosed8–12 yrsfulltimeBengaluru
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Section · 01
About this role
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
Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.
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Section · 02
Skills
AutomationData ingestionclaudeAI evaluationCrewaicloud platformsapisai modelai frameworksamazon web servicesBusiness RequirementsCOSMOSazure cosmos dbAzure OpenAIData LakeData EngineeringcloudArchitectureAi SolutionsMicrosoft AzureArtificial Intelligence
Section · Company
About Moody's Analytics
Moody's Analytics
Analytics & KPO
761
employees
2007
19 years old
New York, New York
United States (USA)
₹17.5L PA avg
Avg at Moody's Analytics
About
Moody's Analytics provides financial intelligence and analytical tools supporting our client's growth, efficiency and risk management objectives.
Industries
Analytics & KPOFinancial Services
Employee ratings
10 reviews
4.0/ 5
Culture
3.6
Career growth
3.2
Work-life
3.5
Employees rate it well for
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