Luxoft India
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Hyderabad · On-site · 1-3 Yrs · Sep 18
Information Technology Support Services · 10000+ Employees · Zug, Switzerland
Luxoft, a DXC Technology Company, is a digital strategy and software engineering firm providing bespoke technology solutions that drive business change for customers globally. As part of DXC Technology (NYSE: DXC), Luxoft enables business transformation, enhances customer experiences, and boosts operational efficiency through its strategy, consulting, and engineering services. Luxoft combines a unique blend of engineering excellence and deep industry expertise to serve clients globally, specializing in automotive, financial services, travel and hospitality, healthcare, life sciences, media and telecommunications. Luxoft is well known for its consistent high level of delivery and complex project management, its premier digital engineering talent, exceptional client focus, and agility, creativity, and remarkable problem-solving capabilities.
Hiring: Engineering
What employees say
+ Better work life balance, Good environment, Manager are supportive
− No hike, no promotion etc
More reviews on the company profile.
Interview experience · Lead Data Engineer
Hacker rank exam where 3 -4 portions Sql, pyspark, databricks Technical round : majorly in optimization Pm round : just for very forma discussion Client round : very very strong round , These 4 rounds is minimum
Average difficulty · Noida · 126 reports
More interview insightsJob description
Overview
Project description
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.
- 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.
Qualifications
- Ontology Design & Maintenance: Design, develop, and maintain ontologies (using OWL/RDF or similar).
- Knowledge Graph Experience: Practical experience building or maintaining knowledge graphs or linked data systems in an enterprise setting.
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.
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Job application FAQ
Last updated 30 September 2026
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- Is Semantic AI/ML Architect remote or on-site in Hyderabad, Telangana?
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- What skills matter for this Semantic AI/ML Architect role at Luxoft India?
- This is a mid-level Semantic AI/ML Architect role. Key skills include skills listed in the job description. Feedback AI highlights matched vs missing skills on your profile after signup.