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Job Details:

Job Description Summary

The Senior Staff Enterprise Technical Architect evaluates, creates, and maintains detailed technical patterns, platforms, and services that support technology and business capabilities, including those leveraging Artificial Intelligence (AI) and Machine Learning (ML). ETAs assist project/product teams in designing or building highly complex application and infrastructure components, with a focus on integrating AI/ML capabilities securely and scalable. ETAs evaluate and guide the organization to or away from emerging technologies based on projected benefit and TCO, specifically assessing emerging AI/ML technologies, models, and frameworks, and document and teach both new and established patterns to engineering teams. Enterprise Technical Architects, in collaboration with Enterprise Architects, own and manage the technology portfolio, including the enterprise AI/ML platform and related services. Senior Staff Architects in Enterprise Technical Architecture oversee multiple technology stacks, now explicitly including AI/ML technologies.

Essential Duties and Responsibilities:

•  The scope of coverage includes multiple related tech stacks (e.g., SAP, Cloud Native, EDI, Integrations, Security, Mainframe, AI/ML Platforms, Generative AI Services, Vector Databases, MLOps).

• Scope of Influence is across multiple partner technical teams. Forward-looking to new patterns, especially in the application of AI/ML. Governance is mostly strategic. Able to influence Dir / Sr. Dir levels.

• Training and mentorship within an area of expertise plus the same on architectural standards & processes, including AI/ML architecture patterns and best practices.

• Perform other assigned job-related duties that align with our organization's vision, mission, and values and fall within your scope of practice.

Technical Standards and Governance:

• Evaluate, Establish, Coach/Train, and maintain enterprise-wide technical standards, guidelines, and best practices for software development, quality, and infrastructure, specifically extending these to cover AI/ML model development, deployment, MLOps, and responsible AI practices.

• Ensure compliance with these standards across all programs and projects, particularly for AI-enabled solutions.

Architecture Design and Review & Portfolio Management:

• Design scalable, reliable, and secure architecture solutions that align with business goals and technical requirements, with a specific focus on integrating AI/ML capabilities into enterprise systems and data flows.

• Conduct architecture reviews and provide guidance to ensure alignment with enterprise standards, including reviews of AI/ML solution designs.

• Maintain technical portfolio in one’s area in collaboration with SW Engineering teams and leaders, including the evaluation and management of AI/ML platforms, tools, and services.

Cross-Disciplinary Expertise:

• Leverage software development, quality, and infrastructure engineering expertise to provide holistic architectural solutions, integrating deep knowledge of AI/ML technologies and their lifecycle.

• Ensure seamless integration of development, quality, and infrastructure components, especially as they pertain to building, deploying, and managing AI/ML models and applications.

Technology Evaluation and Selection: • Evaluate and select appropriate technologies, tools, and platforms that align with enterprise standards and business needs, including third-party solutions, with a specific emphasis on evaluating and selecting AI/ML models, platforms (e.g., generative AI cloud services, vector databases), frameworks, and MLOps tools. • Conduct proof-of-concept (PoC) projects to validate technology choices, including PoCs for emerging AI/ML technologies. • Manage relationships with technology vendors and ensure that tools and services meet enterprise standards, particularly for AI/ML vendors and service providers.

Collaboration and Communication:

• Collaborate with development, quality, and infrastructure teams to ensure alignment with enterprise architecture standards, providing expert guidance on AI/ML integration and architecture.

• Facilitate communication and knowledge sharing across teams and departments, educating stakeholders on AI/ML capabilities, risks, and architectural considerations.

Continuous Improvement:

• Promote a culture of continuous improveme