AI Development Architect

SAP•Stanford, CA

27d•$192,500 - $365,600•Hybrid

About The Position

We help the world run better. At SAP, we keep it simple: you bring your best to us, and we'll bring out the best in you. We're builders touching over 20 industries and 80% of global commerce, and we need your unique talents to help shape what's next. The work is challenging – but it matters. You'll find a place where you can be yourself, prioritize your wellbeing, and truly belong. What's in it for you? Constant learning, skill growth, great benefits, and a team that wants you to grow and succeed. You are the technical leader who sets the AI architecture direction and accelerates enterprise-wide adoption of safe, scalable AI systems. You combine deep engineering expertise with strategic influence, working across product, platform, security, data, and business stakeholders. You define reference architectures, drive engineering alignment, and ensure teams can build AI-powered solutions reliably, efficiently, and responsibly. You remain hands-on and outcomes-oriented, capable of prototyping, evaluating emerging AI technologies, and enabling engineering teams to adopt modern AI development patterns—including LLM orchestration, agentic workflows, semantic data architectures, and model governance frameworks. You will report to the Head of Business AI Engineering and serve as a principal advisor and accelerator for AI innovation in the SAP SuccessFactors product.

Requirements

Deep engineering expertise
Strategic influence
Ability to work across product, platform, security, data, and business stakeholders
Ability to define reference architectures
Ability to drive engineering alignment
Ability to ensure teams can build AI-powered solutions reliably, efficiently, and responsibly
Hands-on and outcomes-oriented
Capable of prototyping
Capable of evaluating emerging AI technologies
Ability to enable engineering teams to adopt modern AI development patterns—including LLM orchestration, agentic workflows, semantic data architectures, and model governance frameworks.

Nice To Haves

LLM orchestration
Agentic workflows
Semantic data architectures
Model governance frameworks

Responsibilities

Define and lead the AI architecture strategy, including reference architectures, platform patterns, reusable components, and integration standards.
Architect agentic systems, tool orchestration, and LLM-driven workflows to support intelligent and autonomous application behaviors.
Partner with product engineering leaders to embed AI capabilities into core product experiences and foundational platform services.
Establish AI governance frameworks, covering data lineage, model monitoring, human-in-the-loop controls, auditability, and responsible AI practices.
Guide the selection and adoption of AI platforms, frameworks, and model providers, balancing performance, cost, compliance, and security.
Drive engineering productivity improvements through reusable libraries, development environments, and standardized patterns.
Measure and improve AI cost efficiency, including inference cost modeling and optimization strategies.
Collaborate with global engineering, T&I, Product, and business leadership to ensure aligned technical decisions and scalable execution.
AI Community of practice: Lead AI enablement programs—mentoring engineers, training architects, and promoting knowledge-sharing across teams.
Prototype, validate, and launch reference implementations and accelerator solutions that unlock faster adoption and delivery.
Communicate architectural direction clearly to executives, engineering teams, and cross-functional stakeholders, enabling decision-making at speed.
Enterprise AI Enablement: Own the AI Pattern library, ensuring reusable templates for AI across SuccessFactors
Innovation Pipeline: Awareness, validation and recommendation of emerging AI tools/concepts
Bridge product vision and technical execution: translating AI use cases, concepts and strategic goals into scalable, governed architectures and actionable engineering plans.
Stakeholder Enablement: Act as the bridge between research, engineering and product management to translate emerging AI capabilities into business outcomes
Evangelism: Represent AI architecture direction (across SAP, with a SuccessFactors focus) in executive customer engagements.
Customer & Field Alignment: Support Product Management in customer discussions and supporting material creation to effectively educate and communicate the field and customer base.
Metrics and Outcome Definition: Partner with Product Management to define success metrics and ensure the architecture is in place to enable their measurement