Semantic AI/ML Architect

Luxoft; Gurugram District; Hybrid; INR 2,000,000 - 3,800,000; Full time

Job summary

Luxoft is seeking a Senior Semantic Engineer to design and implement semantic data frameworks that standardize enterprise data across domains.

You will build ontologies, develop knowledge graphs, and enforce SHACL/OWL constraints while collaborating with AI teams to embed semantic models in AI-powered applications. Strong web semantic expertise is essential for enterprise-scale impact.

Qualifications

• Strong knowledge of OWL/RDF and SPARQL for ontology querying.
• Experience building enterprise knowledge graphs and linked data.
• Solid data modelling principles and data integration skills.

Responsibilities

• Design and maintain ontologies to model key enterprise data domains.
• Build and manage enterprise knowledge graphs with linked data sources.
• Create and optimize SPARQL queries and endpoints for analytics.
• Implement semantic validation rules to ensure data quality.
• Collaborate with AI teams to integrate semantic models into AI-powered apps.

Skills

OWL/RDF/SPARQL

Knowledge Graphs

Data modelling

Programming (Python/Java)

Job description

Project description

We are seeking a Senior Semantic Engineer to design and implement semantic data frameworks that provide a shared structure for enterprise data. In this role you will focus on building and maintaining ontologies and knowledge graphs, enforcing semantic validation rules for data quality, and collaborating with AI teams to integrate these semantic structures into intelligent applications. The position is industry-agnostic, emphasizing strong semantic web expertise and the ability to apply it in any enterprise context.

Responsibilities

• Ontology Design & Maintenance: Design, develop, and maintain ontologies (using OWL/RDF or similar) that model key enterprise data domains and relationships, ensuring a consistent and shared data vocabulary across the organization. This includes collaborating with domain experts to capture real-world concepts and validate that the ontology accurately represents business knowledge.
• Knowledge Graph Development: Build and manage enterprise knowledge graphs based on the defined ontologies, linking diverse data sources into a unified graph data model. This involves configuring graph databases or triple stores, populating the knowledge graph with data (RDF triples), and optimizing it for query performance and scalability.
• Semantic Querying (SPARQL): Create and optimize SPARQL queries to enable efficient retrieval, integration, and analysis of data from the knowledge graph. You will develop semantic queries and endpoints that support advanced search and analytics use cases, making it easier for others to retrieve insights from linked data.
• Validation Rules & Data Quality: Implement semantic validation rules and consistency checks (e.g., using SHACL or OWL constraints) to ensure data integrity and quality within the ontology and knowledge graph. You will define and enforce data modelling conventions and business rules so that enterprise data conforms to the ontology's standards and remains interoperable across systems.
• Integration with Enterprise Systems: Work closely with software engineers, data architects, and IT teams to integrate the ontology and knowledge graph into the organization's existing data infrastructure and workflows. This includes embedding semantic models in data pipelines, APIs, and databases, so that enterprise applications can produce and consume linked data seamlessly.
• Collaboration & Cross-Functional Support: Collaborate with cross-functional teams and stakeholders. For example, partner with AI/ML teams to incorporate the knowledge graph into AI-driven solutions, and team up with business analysts or data stewards to align the semantic models with business needs. You will communicate semantic concepts to non-technical stakeholders, providing training or documentation to ensure adoption of the semantic framework across the organisation.
• Integration with AI Agents: Work with AI agents and large language model (LLM) teams to leverage the ontology and knowledge graph for intelligent applications. For instance, you might enable an AI chatbot to use the knowledge graph for more context-aware responses, or develop mechanisms for AI systems to perform reasoning over the ontologies. This responsibility ensures that semantic data structures enhance AI initiatives (e.g. improving context, disambiguation, and knowledge retrieval in AI workflows).
• Standards & Best Practices: Stay current with emerging semantic web standards, tools, and best practices. Continuously improve the semantic architecture by adopting relevant metadata standards and ensuring alignment with industry best practices for ontologies and knowledge graphs. You will also contribute to establishing internal guidelines and best practices for semantic data management, promoting a culture of well-structured, semantically-rich data across the enterprise.

SKILLS

Must have

• Ontology Design & Maintenance: Design, develop, and maintain ontologies (using OWL/RDF or similar).Semantic Web Proficiency: Strong knowledge of semantic web technologies and standards - specifically, hands‑on proficiency with OWL (Web Ontology Language) and RDF (Resource Description Framework) for ontology modelling, as well as SPARQL for querying graph data.Knowledge Graph Experience: Practical experience building or maintaining knowledge graphs or linked data systems in an enterprise setting.Data Modelling & Integration Skills: A solid understanding of data modelling principles, data architecture, and integrating heterogeneous data sources. You should be capable of abstracting real-world entities into a semantic schema and mapping relational or NoSQL data to an ontology.Programming Skills: Proficiency in at least one programming or scripting language (such as Python, Java, or similar)

Nice to have

• Metadata Standards: Familiarity with metadata standards and vocabularies such as Dublin Core, schema.org, or other industry-specific ontologies/taxonomies. Experience applying these standards to annotate or integrate data
• AI and LLM Integration: Experience working on projects that involve AI agents or large language models, where ontologies or knowledge graphs were used to improve AI performance.
• Enterprise System Integration: Proven experience integrating semantic technologies into existing enterprise systems or data platforms.
• Tools & Platforms: Hands‑on experience with ontology and knowledge graph tools is beneficial.