Responsibilities
- Act as the primary authority on enterprise-wide architecture spanning applications, data systems, AI platforms, integrations, cloud infrastructure, and digital solutions.
- Create and manage enterprise architecture principles, technical standards, reference models, governance processes, and long-term technology roadmaps.
- Define and enforce technology standards across software, data, cloud, integration, and artificial intelligence engineering disciplines.
- Lead architecture review boards and provide technical oversight for high-impact technology programs.
- Assess emerging technologies and recommend strategies that advance modernization, innovation, scalability, and efficiency.
- Lead efforts to streamline technology portfolios by reducing complexity, removing duplication, and enhancing system interoperability.
- Develop and govern the enterprise data architecture strategy, covering data lake houses, data products, canonical models, master data, metadata, and governance practices.
- Set standards for Azure Databricks, Delta Lake, Unity Catalog, Azure Data Lake Storage Gen2, DataOps, and AI-optimized data platforms.
- Make strategic architecture decisions for machine learning, predictive analytics, generative AI, retrieval-augmented generation (RAG), vector databases, semantic search, agentic AI, and intelligent automation.
- Establish enterprise-wide AI architecture standards covering model governance, responsible AI, security, operations, and platform integration.
- Advise on the design of data products that enable analytics, automation, reporting, and business intelligence.
- Ensure enterprise data is secure, governed, discoverable, reusable, and compliant with applicable regulations.
- Define the enterprise intelligence architecture strategy to support analytics, BI, machine learning, generative AI, agentic AI, automation, and decision intelligence.
- Set standards for AI-ready data products, semantic layers, knowledge management, vector stores, RAG, AI orchestration, and agent-based architectures.
- Design reference models for AI assistants, copilots, intelligent workflow automation, agentic AI, predictive analytics, and decision support systems.
- Lead the deployment of enterprise AI platforms using Azure AI Services, Azure OpenAI, Databricks AI, Azure Machine Learning, MLflow, vector databases, and related tools.
- Develop governance frameworks for responsible AI, model lifecycle, security, monitoring, explainability, and regulatory compliance.
- Collaborate with business leaders to identify opportunities for transforming operations using AI, machine learning, automation, and advanced analytics.
- Define architectural patterns for human-in-the-loop AI, autonomous agents, intelligent document processing, workflow orchestration, and enterprise automation.
- Guide the shift from traditional reporting to decision intelligence and AI-powered business operations.
- Establish standards for knowledge architecture, semantic search, document enrichment, embeddings, vectorization pipelines, and AI knowledge repositories.
- Evaluate new AI technologies and define enterprise adoption strategies that balance value, risk, and compliance.
- Define architecture standards for enterprise applications, digital products, APIs, microservices, event-driven systems, and cloud-native solutions.
- Work with engineering leadership to establish modern software architecture approaches that ensure scalability, maintainability, performance, and resilience.
- Support development teams in adopting modern engineering practices such as API-first design, cloud-native development, event-driven architecture, domain-driven design, and microservices.
