Java Software Engineer - AI & Data Platforms (6-9Yrs)

Infor Inc.; Hyderabad; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

Infor Inc. is building a Data Fabric for cross-ERP integration, leveraging RDF/OWL ontologies and SPARQL/Jena-based services to enable scalable, multi-tenant analytics.

You will design ontology models, implement canonical data schemas, and craft SPARQL/Jena services, while guiding junior engineers and aligning with architecture stakeholders. This role focuses on semantic data platforms and governance within an enterprise context.

Qualifications

• 6+ years of software engineering experience in enterprise applications, platforms, or data integration environments.
• 4+ years of hands-on Java development experience with Spring Boot, REST APIs, and SQL.
• Strong knowledge of semantic technologies, including RDF/OWL, SPARQL, Apache Jena, or similar graph/ontology frameworks.
• Experience in data modeling, schema mapping, canonical data models, and master data management across multiple systems.
• Exposure to cloud platforms (AWS preferred), CI/CD pipelines, application deployment, and monitoring.
• Ability to design scalable integration solutions and communicate technical concepts to diverse stakeholders.
• Experience mentoring engineers and providing technical guidance and code reviews.
• Strong analytical, problem-solving, ownership, and collaboration skills.
• Experience working in Agile development environments and cross-functional teams.

Responsibilities

• Design and develop ontology models (RDF/OWL) and canonical data models for cross-ERP integration domains, including Customer Master, Order-to-Cash, and Financials.
• Develop and optimize SPARQL queries and Apache Jena (TDB2) services for large-scale semantic data management and access.
• Lead ERP-to-canonical model mapping initiatives, handling schema variations, multi-tenant requirements, and schema evolution.
• Create SHACL validation rules and data quality frameworks to ensure consistency and integrity of ontology and canonical model artifacts.
• Mentor Software Engineers on ontology modeling, semantic technologies, and canonical data modeling best practices.
• Contribute ontology and canonical artifacts supporting Data Fabric ETL, streaming pipelines, and future GenAI/RAG knowledge graph initiatives.
• Participate in architecture reviews, drive design discussions, and support governance for ontology versioning and canonical schema management.
• Collaborate with product, engineering, and quality teams to deliver scalable semantic data solutions across enterprise platforms.

Skills

Mentoring

Communication

Problem solving

Ownership

Collaboration

Agile methods

Tools

RDF/OWL

SPARQL

Apache Jena

SHACL

Protégé

Fuseki

SQL

Java

Spring Boot

Job description

Java Software Engineer - AI & Data Platforms (6-9Yrs)

Department: Development

Employment Type: Full Time

Location: Hyderabad

Description

Infor is building a new Data Fabric capability that bridges structural data gaps across Infor's ERP products through a canonical data model, and elevates that canonical data into unified business intelligence through an ontology layer built on Apache Jena and SPARQL. Together these capabilities lower cross-application integration cost, unlock multi-tenant analytics, and provide the semantic foundation required to scale Generative AI from Data Fabric.

As a Senior Software Engineer, you will design and build core pieces of this semantic layer: ontology models, canonical entity schemas, and the SPARQL/Jena-based services that expose them, while mentoring Software Engineers and partnering closely with the Team Leader and architecture stakeholders.

A Typical Day in the Life Includes:

• Design and develop ontology models (RDF/OWL) and canonical data models for cross-ERP integration domains, including Customer Master, Order-to-Cash, and Financials.
• Develop and optimize SPARQL queries and Apache Jena (TDB2) services for large-scale semantic data management and access.
• Lead ERP-to-canonical model mapping initiatives, handling schema variations, multi-tenant requirements, and schema evolution.
• Create SHACL validation rules and data quality frameworks to ensure consistency and integrity of ontology and canonical model artifacts.
• Mentor Software Engineers on ontology modeling, semantic technologies, and canonical data modeling best practices.
• Contribute ontology and canonical artifacts supporting Data Fabric ETL, streaming pipelines, and future GenAI/RAG knowledge graph initiatives.
• Participate in architecture reviews, drive design discussions, and support governance for ontology versioning and canonical schema management.
• Collaborate with product, engineering, and quality teams to deliver scalable semantic data solutions across enterprise platforms.

Basic Qualifications:

• 6+ years of software engineering experience in enterprise applications, platforms, or data integration environments.
• 4+ years of hands-on Java development experience with Spring Boot, REST APIs, and SQL.
• Strong knowledge of semantic technologies, including RDF/OWL, SPARQL, Apache Jena, or similar graph/ontology frameworks.
• Experience in data modeling, schema mapping, canonical data models, and master data management across multiple systems.
• Exposure to cloud platforms (AWS preferred), CI/CD pipelines, application deployment, and monitoring.
• Ability to design scalable integration solutions and communicate technical concepts to diverse stakeholders.
• Experience mentoring engineers and providing technical guidance and code reviews.
• Strong analytical, problem-solving, ownership, and collaboration skills.
• Experience working in Agile development environments and cross-functional teams.

Preferred Qualifications:

• Prior work on knowledge graphs, ontology engineering, or semantic data platforms in an enterprise or ERP context.
• Experience with SHACL, Protégé, or Fuseki.
• Familiarity with multi-tenant SaaS architecture and its implications for canonical/semantic data design.
• Exposure to GenAI/LLM patterns such as retrieval-augmented generation (RAG) over knowledge graphs.