AI Engineer

• Own and Evolve the Semantic Architecture for the Bank.
• Design, maintain, and govern AI ready semantic architecture and underlying technical framework.
• Working with domain experts and architects, establish naming conventions, ontologies, and data modeling rules & taxonomies.
• Build and maintain a canonical model that enables dynamic reconstruction of entire customer journeys across our products and channels.
• Define the domain event ontology and event schemas ensuring consistent semantics across all transactional, behavioral, and system events.
• Determine a robust metadata architecture and framework and ensure seamless integration with our foundation data platform Databricks.
• Define and manage the semantic runtime, ensuring semantic definitions are performant, executable, testable, queryable, and consistently applied across ETL, feature pipelines, LLM retrieval workflows, MCP Server and real-time APIs.
• Design the semantic graph (entities, relationships, hierarchies, business rules) that forms the backbone for reasoning, AI retrieval, event linking, and customer journey reconstruction & ultimately AI taking actions.
• Develop a framework to embed semantics capture through data product development in a frictionless and consistent manner.
• Develop intelligent AI assisted solutions to ensure semantic consistency with Data Product or Domain SME in the loop.
• Develop solutions to detect and remediate semantic drift.
• Own the product roadmap for semantic tooling & frameworks - such as semantic catalog, semantic contract validator, lineage resolver, semantic drift detector, and ontology editing/visualisation tools, canonical model builder.
• Define the Semantic and Data Architecture for “Shared” Data Products such as Master Data, Reference Data, Feature Store, Regulatory Data Products, Core Product data such as User, Session, Channel.
• Ensure interoperable, distributed ownership aligned with Data Mesh principles wherever practical.
• Design semantics layer to power Classic and Modern AI (LLM) large scale workloads and usecases.
• Define semantic grounding strategies for LLMs, enabling hybrid retrieval (SQL + vector + ontology), context resolution, and reliable semantic augmentation for AI Agents.
• Develop semantics layer that powers reliable and trust worthy conversational analytics e.g. AI Genie in Databricks or custom AI Conversational Data Agents.
• Enable frameworks to ensure efficient and effective semantic consumption as part of Context Engineering and Machine learning.
• Coach DPOs, Stewards, Architects, and Engineers on semantic best practices.
• Define templates, frameworks, and accelerators to platform teams for automation.
• Drive reusable semantic building blocks across product teams.
• Collaborate with Platform, Governance, and Engineering teams to turn your ideas into reality.
• Ensure semantics integrate with Databricks, Unity Catalog, DLT, dbt, and AI tools.
• Align semantic models with quality, security, lineage, and access policies.

Semantics Architect – Data & AI Platform

Applied AI Engineer