Data & AI Architect – BD Excellence; BDE) Office

Job Description Summary

This position serves as a technical expert responsible for architecting and delivering AI-powered solutions that enable operational excellence, analytics, and decision-making across the BDE ecosystem. The role owns end-to-end solution design and implementation, including data integration, AI enablement, analytics delivery, and technology standards. Success is measured through scalable adoption, improved insight generation, reduced manual effort, and accelerated decision support.

• Design end-to-end AI architectures that integrate structured and unstructured enterprise data sources.
• Define scalable patterns for AI integration across collaboration, analytics, workflow, and internal platforms.
• Develop and deploy AI-enabled solutions using programming languages, APIs, cloud services, and modern frameworks, including large language model (LLM) and agentic AI architectures.
• Build and maintain automated data pipelines, connectors, and synchronization processes, leveraging modern data engineering platforms (e.g., Databricks, Snowflake).
• Prototype and move into production capabilities including document intelligence, analytics copilots, natural language querying, and insight generation.
• Partner with analytics and data teams to optimize data models and architectures for AI use cases.
• Apply AI and advanced analytics to identify insights, trends, and leading indicators supporting operational excellence.
• Enable embedded and conversational analytics experiences within existing business workflows and platforms.
• Define and promote technical standards, reusable components, and best practices across the BDE ecosystem.
• Mentor developers and analysts on AI solution development and integration approaches.

• Execution Excellence:
  Delivers scalable solutions from concept through production deployment.
• Functional Expertise:
  Deep knowledge of AI architecture, software engineering, analytics, and data integration.
• Stakeholder Leadership:
  Builds strong partnerships across business and technical teams.
• Influence Without Authority:
  Drives adoption of standards and solutions across matrixed environments.
• Operational Rigor:
  Ensures alignment with governance, security, and responsible AI requirements.
• Data-Driven Decision Making:
  Applies analytics and insights to improve outcomes and performance.
• Strategic Communication:
  Translates complex technical concepts into actionable business recommendations.
• Change Leadership:
  Supports adoption of new capabilities and ways of working.
• Continuous Improvement Mindset:
  Identifies opportunities to optimize processes, solutions, and user experiences.

Education:

Bachelor's degree in Computer Science, Engineering, Data Science, Information Systems, or a related field required. Master’s degree in Computer Science, Artificial Intelligence/Machine Learning, Data Science, or a related quantitative/technical field strongly preferred.

Experience:

10+ years of progressive experience in software engineering, data engineering, or AI/ML architecture, with a demonstrated track record of designing and deploying production-grade, end-to-end AI solutions. Hands-on experience architecting AI agents, copilots, chatbots, or retrieval-augmented generation (RAG) solutions using LLM APIs (e.g., Claude, Azure OpenAI, or equivalent). Practical experience with modern data engineering and analytics platforms (e.g., Databricks medallion/lakehouse architecture, Delta Lake, PySpark, Snowflake).

Experience integrating multiple enterprise data sources and systems through APIs, connectors, and automated data pipelines. Proven ability to move from concept to production-ready solutions in complex, matrixed enterprise environments. Experience applying responsible AI, data governance, and security/compliance frameworks (e.g., SOC2, GDPR, HIPAA, or equivalent) is a plus, particularly in regulated industries.

• Strong hands-on development experience with Python and SQL; working knowledge of cloud platforms (AWS, Azure, or GCP).
• Practical experience building and orchestrating GenAI/agentic solutions using frameworks such as Lang Graph, Model Context Protocol (MCP), or comparable agent-orchestration and multi-agent tooling.
• Knowledge of analytics architectures, data modeling, and insight generation, including natural language querying and embedded/conversational analytics.
• Understanding of model lifecycle management, evaluation, monitoring, and continuous…