Software Engineer

Key Responsibilities

• Design, build, and maintain agentic AI pipelines (using Google ADK or similar frameworks) to automate semantic mapping, dimension mining, and ontologydriven reasoning.

• Create and evolve enterprise ontologies in RDF/OWL, including upper ontologies and domain extensions aligned to CIM (where applicable), to enable reusable enterprise semantics.

• Engineer LLMpowered services for schema understanding, semantic alignment, ontology enrichment, and AIassisted metadata generation, with a focus on accuracy, traceability, and scale.

• Implement SPARQL querying and reasoning layers over knowledge graphs to drive downstream transformations and ensure consistent interpretation of business concepts.

• Architect and deliver Pythonbased microservices and batch pipelines that integrate semantic reasoning with modern dataengineering workflows.

• Build and optimize dimension and fact generation pipelines on Microsoft Fabric (Lakehouse, Spark, SQL, orchestration) to produce businessready star schemas from heterogeneous sources.

• Define and enforce engineering standards, design patterns, and reusable components for semantic and AIdriven data platforms (quality, observability, security, and performance).

• Partner with data architects, domain SMEs, and governance teams to validate semantic definitions, manage change, and ensure platform scalability and adoption.

• Conduct code reviews, mentor engineers, and influence technical decisions across the platform to raise engineering quality and delivery velocity.

Required Skills & Qualifications:

• 6+ years of professional software engineering experience, with strong proficiency in Python and GenAI.

• Handson experience building LLMbased systems using commercial or opensource models.

• Solid understanding of semantic technologies: RDF, OWL, ontologies, knowledge graphs, and SPARQL.

• Experience designing or working with agentic AI frameworks (e.g., Google ADK, LangChain agents, or similar).

• Strong background in data engineering concepts (ETL/ELT, star schemas, metadatadriven pipelines).

• Experience building and operating systems on cloud platforms, preferably Microsoft Azure / Microsoft Fabric.

• Strong problemsolving skills and ability to work in ambiguous, greenfield platform initiatives.

Preferred Skills:

• Experience with enterprise data models (e.g., CIM or canonical models).

• Familiarity with semantic alignment, ontology mapping, or data cataloguing tools.

• Exposure to MLOps / LLMOps, model evaluation, and AI observability.

• Knowledge of distributed systems, CI/CD pipelines, and containerisation.

• Experience building AIassisted analytics or semantic layers for BI or NLQ use cases.