AI Principal Architect II

AI Principal Architect II

Position Summary:

The Data, Analytics, and AI Principal Architect II is a senior architect role responsible for defining and driving the strategic vision for enterprise data and AI systems. This position oversees the design and implementation of scalable, secure, and innovative data platforms and AI capabilities that empower business intelligence, advanced analytics, and machine learning across the organization.

Key responsibilities include:

• Develop and own the enterprise data and AI architecture roadmap aligned with business goals.
• Lead the design and implementation of scalable data platforms, analytics solutions, and AI/ML infrastructure.
• Define standards and best practices for data governance, metadata management, and model lifecycle management.
• Partner with engineering, product, and business teams to ensure seamless integration of data and AI capabilities.
• Evaluate emerging technologies and trends in data, analytics, and AI to inform strategic decisions.
• Oversee architectural reviews, technical assessments, and solution design for complex data initiatives.
• Ensure compliance with data privacy, security, and regulatory requirements.
• Build and mentor a high‑performing team of data and AI architects.

• Model development and deployment using frameworks such as Tensor Flow, PyTorch, or Scikit‑learn.
• Familiarity with cloud‑based ML platforms, such as AWS Sage Maker, Azure Foundry, or Google AI Platform, Mosaic AI.
• Experience in utilizing tools like MLflow for managing the ML lifecycle, including experimentation, reproducibility, and deployment.
• Knowledge of natural language processing (NLP) and/or reinforcement learning techniques as applicable to business use cases.
• Ability to integrate advanced analytics and AI/ML models into existing data workflows, ensuring seamless access and usability.

• Lead the assessment and selection of data and AI software platforms, tools, and frameworks to ensure alignment with architectural standards and business needs.
• Establish a formal process for evaluating vendor solutions, open‑source technologies, and proprietary platforms for scalability, interoperability, and long‑term viability.
• Drive innovation by piloting new technologies and proof‑of‑concept initiatives that explore advanced analytics, generative AI, and automation opportunities.
• Collaborate with R&D and innovation teams to identify use cases where emerging software can deliver competitive advantage or operational efficiency.
• Maintain a technology radar to track and communicate the maturity and relevance of new tools and platforms across the data and AI landscape.

• Foster strong partnerships across business units, IT, and external vendors to align data and AI strategies with enterprise priorities.
• Act as a trusted advisor to senior leadership, translating complex technical concepts into actionable business insights.
• Facilitate cross‑functional collaboration to ensure shared ownership of data‑driven initiatives and seamless execution.
• Represent the data and AI architecture function in enterprise governance forums, steering committees, and strategic planning sessions.

• Champion a data‑driven culture by promoting the value of analytics and AI across the organization.
• Influence stakeholders to adopt modern data and AI practices through education, storytelling, and strategic alignment.
• Lead change management efforts to support the adoption of new technologies, platforms, and processes.
• Advocate for continuous improvement and innovation in data and AI architecture to drive long‑term transformation.

• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or related field.
• 10+ years of experience in data architecture, analytics, and AI/ML systems.
• Proven leadership in designing enterprise‑scale data platforms and AI solutions.
• Experience leading architecture teams.
• Deep expertise in cloud platforms (Azure, AWS, GCP), data lakes, data warehouses, and real‑time data processing.
• Strong understanding of AI/ML frameworks (Tensor Flow, PyTorch), MLOps, and model deployment…