Key Responsibilities :

- Design, build, and maintain agentic AI pipelines (using Google ADK or similar frameworks) to automate semantic mapping, dimension mining, and ontology-driven 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 LLM-powered services for schema understanding, semantic alignment, ontology enrichment, and AI-assisted 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 Python-based microservices and batch pipelines that integrate semantic reasoning with modern data-engineering workflows.

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

- Define and enforce engineering standards, design patterns, and reusable components for semantic and AI-driven 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.

- Hands-on experience building LLM-based systems using commercial or open-source 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, metadata-driven pipelines).

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

- Strong problem-solving 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 AI-assisted analytics or semantic layers for BI or NLQ use cases.