Director, AI, Data & Enterprise Architecture

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Be a Part of Something BIG!

The Senior AI Data and Enterprise Architect/Director is responsible for designing and governing the enterprise-wide architecture that enables trusted data, scalable analytics, and production-grade AI—including machine learning and generative AI— across the organization.

This role aligns business strategy, operating models, and technology platforms to deliver secure, compliant, and cost-effective solutions.

You will define target-state architectures, reference patterns, and roadmaps across data platforms, AI/ML lifecycle (MLOps/LLMOps), integration, security, and governance. You will partner with business leaders, product teams, engineering, security, and risk/compliance to ensure AI and data solutions are reliable, explainable, and aligned to enterprise standards.

• Lead end-to-end architecture for enterprise data and AI capabilities aligned to business strategy and measurable outcomes.
• Produce target-state architectures
  , current-state assessments
  , and multiyear roadmaps spanning platforms, applications, integration, and operating model.
• Define and maintain reference architectures
  , standards
  , and guardrails for data, analytics, AI/ML, GenAI, and integration patterns.
• Facilitate architecture governance: design reviews, standards enforcement, exception handling, and technical decision records.

• Architect scalable data solutions (e.g.,
  Lakehouse/data warehouse
  , streaming/event ingestion, semantic/metrics layer).
• Establish data modelling standards (conceptual/logical/physical), including domain-oriented data products and/or data mesh patterns as appropriate.
• Design enterprise-grade metadata management
  , lineage
  , Catalog
  , data quality
  , MDM
  , and privacy controls.
• Create patterns for real-time/near-real-time analytics, including event-driven architectures and streaming (e.g., Kafka/Kinesis/Pub Sub).

• Define architecture for the full AI lifecycle: experimentation, training, deployment, monitoring, and continuous improvement.
• Design MLOps/LLMOps frameworks for reproducibility, CI/CD, feature stores, model registries, drift monitoring, and automated evaluation.
• Architect GenAI solutions (where relevant):
  RAG
  , vector search, orchestration, prompt/version management, safety filters, and model evaluation.
• Establish patterns for model governance: approvals, auditability, explainability, bias testing, and model risk controls.

• Translate business problems into ML solutions (classification, ranking, nextbest-action, anomaly detection, forecasting, summarization).
• Perform feature engineering for streaming and batch contexts (rolling metrics, aggregations, stateful features).
• Train, evaluate, and tune models; define KPIs and acceptance criteria (latency, precision/recall, lift, business impact).
• Develop and iterate on LLM and agent approaches
  :
• Prompting and structured context design
• Evaluation harnesses (offline + online)
• Guardrails, grounding, and hallucination reduction
• Design experimentation frameworks (A/B tests, shadow mode, canaries) and model monitoring strategies (drift, bias, stability).

• Define integration patterns across enterprise systems (APIs, events, ETL/ELT, iPaaS) to enable reliable data and AI workflows.
• Collaborate with application and infrastructure architects to align data/AI platforms with enterprise principles (observability, resiliency, cost controls).
• Influence vendor selection and platform decisions; evaluate trade-offs across cloud services, open-source tooling, and commercial platforms.

• Act as a trusted advisor to executives and senior stakeholders—translating business goals into technical architecture and investment strategy.
• Mentor…