Snowflake Data Architect (With AI experience)

About the Role

We are looking for an AI Data Architect to design, build, govern, and evolve the single source of truth that powers every AI initiative in our organization. This platform will serve as the foundational nervous system for conversational AI assistants, dashboard intelligence, autonomous AI agents, RAG-powered applications, predictive ML models, and any AI product we build today or in the future. The resource will architect the system, drive implementation, own the data contracts that agents and AI applications depend on, enforce security and access governance for both human and agent consumers, and continuously monitor and improve the accuracy and reliability of AI outputs that flow from this platform.

Key Responsibilities

• AI-Ready Data Platform — The Single Source of Truth: Architect and own the enterprise AI data platform — the unified, governed layer that ingests, transforms, stores, and serves all data consumed by AI systems across the organisation. Design multi-domain data models (lakehouse, data mesh, event-driven) that are structured from day one to serve AI workloads: clean lineage, versioned schemas, well-documented contracts, and low-latency serving APIs. Own the full data stack: real-time streaming (Kafka, Spark Structured Streaming), batch processing (Databricks, PySpark, Delta Lake), cloud storage and compute (AWS, Azure), and data quality / metadata management. Ensure this platform is the single, authoritative data source for all downstream consumers — conversational AI, dashboard assistants, autonomous agents, ML models, and reporting — eliminating data silos and conflicting truths. Drive modernisation of legacy pipelines (on-prem ETL, batch DWH) to cloud-native, AI-ready architectures with measurable improvements in cost, latency, and delivery velocity.
• Semantic Models & Knowledge Layer: Design the semantic layer that sits above raw data — business-aligned ontologies, entity relationships, domain taxonomies, and knowledge graphs — so AI systems understand context, not just tokens. Build and maintain knowledge graphs (Neo4j or equivalent) that capture relationships between business entities, policies, KPIs, hierarchies, and domain rules — enabling structured reasoning alongside unstructured retrieval. Define and govern a feature store and semantic data contracts that serve both classical ML models and LLM-based applications from a single, well-versioned, trusted source. Own metadata management, data lineage, and audit trails across the semantic layer — ensuring every AI system can trace its outputs back to source data with full accountability.
• RAG, Vector & Retrieval Infrastructure: Design the retrieval infrastructure that powers RAG-based AI applications: embedding pipelines, vector stores (Pinecone, FAISS, ChromaDB, OpenSearch), chunking strategies, and hybrid retrieval layers combining semantic search with structured queries. Define the data contracts between the AI data platform and retrieval consumers — ensuring consistent, freshness-guaranteed, well-indexed data surfaces to RAG pipelines, conversational AI, and agent tools. Architect retrieval systems that balance precision, recall, latency, and cost — with clear evaluation benchmarks, not just infrastructure defaults.
• ML/LLMOps Infrastructure: Own the ML and LLMOps data infrastructure: training data curation pipelines, feature engineering, model evaluation and monitoring, and deployment automation.