Principal Enterprise Architect

Role Definition

Field Detail

Title Principal Enterprise Architect — AI Specialty (Platforms & Services / Hi-Tech)

Reports to Chief Architect / VP of Enterprise Architecture (or CTO office / Delivery leadership)

Level Principal / Distinguished Individual Contributor (most senior IC architecture band); recognized technical authority

Function

Sets AI architecture strategy across a portfolio of Platforms & Services and Hi-Tech accounts; the senior technical authority in front of clients and across delivery teams

Accounts served

SaaS, fintech & payments, and enterprise-platform clients (e.g., Salesforce, DocuSign, PayPal, Stripe, BlackLine, Intuit) and Hi-Tech / semiconductor (e.g., Applied Materials)

Scope

Cross-account and cross-domain — customer, commercial, finance, and operations platforms plus the data and AI/agentic layer that spans them

The Role in One Sentence

You are the senior AI architecture authority for a portfolio of Platforms & Services and Hi-Tech accounts — defining how agentic and machine-learning capabilities are grounded in each client’s data, governed responsibly, and standardized into reusable reference patterns, so that AI becomes a dependable layer of the client’s platform rather than a collection of disconnected pilots.

Key Responsibilities

Architecture Strategy & Vision

AI & Agentic Systems Architecture

Domain Architecture (Platforms & Services and Hi-Tech)

Governance, Standards & Responsible AI

Leadership & Enablement

Architecture Strategy & Vision

• Set the enterprise-wide AI architecture vision and target-state roadmap, and ensure every solution aligns to the broader enterprise-architecture framework.
• Define target-state architecture roadmaps for each account that move clients toward AI-augmented operations, intelligent automation, and better customer and employee experiences.
• Recommend architecture changes in response to technology and business trends; research emerging technologies and advise leadership on their business impact.
• Act as the primary strategic liaison between executive business stakeholders and technical leadership — conceptualizing, defining, and governing overarching system solutions.

AI & Agentic Systems Architecture

• Architect complex, multi-layered AI systems — scalable, resilient, secure, and explainable — tailored to each client’s platform and mission-critical constraints.
• Design reference architectures for GenAI and autonomoagentic frameworks that automate intelligent workflows across customer service, sales/commerce, finance and accounting, risk, and engineering/operations.
• Define how AI agents are grounded in enterprise context (customer data, product/catalog, financial and transaction data, operational telemetry) and how they orchestrate actions safely across SaaS and internal systems via APIs.
• Identify and prioritize AI/ML and GenAI use cases across the two verticals — e.g., fraud detection and risk decisioning, payments and revenue/close automation, customer-support copilots, document intelligence; and for Hi-Tech, yield and manufacturing analytics, EDA/design-flow assistance, and knowledge retrieval over engineering data.
• Standardize the building blocks — LLM onboarding, RAG pipelines, vector stores, and agent/application patterns — into reusable assets that accelerate delivery across accounts.
• Build quick, hands-on proofs of concept and working prototypes to validate feasibility, de-risk architecture decisions, and win client confidence before committing teams to a full build. You prove concepts by building, not just by slide.

Domain Architecture (Platforms & Services and Hi-Tech)

• Bring credible domain range across the account base: SaaS platforms and their extension/integration models; fintech and payments (transaction processing, ledgers, identity/KYC, fraud and risk decisioning, dispute and compliance flows); finance-and-accounting automation (close, reconciliation, controls); and Hi-Tech / semiconductor workflows (design, verification, manufacturing and yield analytics, supply chain).
• Design secure, cloud-native, high-availability architectures — microservices, event-driven patterns (e.g., Kafka), containerization (Kubernetes/Docker) across AWS, Azure, and/or Google Cloud Platform — that meet enterprise reliability and low-latency requirements.
• Promote reuse, open APIs, and interoperability across each client’s platform landscape, integrating AI with systems such as Salesforce, DocuSign, payment platforms, ERP/finance systems, and ServiceNow rather than building in isolation.

Governance, Standards & Responsible AI

• Establish and run architecture governance — standards, principles, patterns, and reference architectures ensuring consistency, scalability, security, and data integrity.
• Present and defend designs through the Architecture Review Board and Design Review Board; make sound integration decisions grounded in open/industry standards.
• Embed AI governance, responsible-AI practices, data privacy, and domain-specific compliance into every AI design — e.g., PCI-DSS, SOX/financial-controls, data protection and fraud/resilience requirements for fintech and payments, and IP/data-sensitivity controls for Hi-Tech.
• Define the model-evaluation and benchmarking standards that AI systems must meet to be approved for production — quality, safety, groundedness, drift, and cost — and make evaluation a required gate, not an afterthought. Critically assess model and vendor performance claims rather than taking them at face value.
• Drive technology-portfolio rationalization and standardize architecture patterns across cross-functional teams.

Leadership & Enablement

• Provide technical leadership and mentorship to solution architects and engineering teams; foster a culture of technical excellence and strategic thinking.
• Lead conversations with client executives (including C-level), business, and technical audiences — acting as a trusted technical advisor, translating business strategy into architecture, and connecting technology to measurable business value.
• Support pre-sales and solutioning — shaping proposals, estimates, and solution architectures, and running discovery and design workshops that turn client pain points into scoped, winnable engagements.
• Partner across delivery teams, engineering, data, product, and client stakeholders to conceive, land, and evolve reference architectures and platform roadmaps that carry across accounts.

Required Qualifications

Experience

Technical Skills (Must-Have)

Technical Skills (Strong Plus)

Non-Technical Skills (Equally Critical)

Education

What Success Looks Like

Experience

• 12–15+ years in IT/enterprise architecture, with multiple years delivering architecture for large, complex programs at enterprise scale.
• Proven track record architecting enterprise-grade AI solutions leveraging cloud-native architectures (AWS, Azure, and/or Google Cloud Platform).
• Substantial experience serving Platforms & Services and/or Hi-Tech clients — SaaS, fintech/payments, or semiconductor/capital-equipment — with enough domain depth in at least one to be immediately credible and the range to ramp quickly on the others.
• Client-facing / consulting experience at a services firm or in an equivalent account-serving role, including pre-sales, solutioning, and executive engagement.
• Hands-on enough to personally stand up a quick proof of concept or prototype — not solely a whiteboard architect — and a track record of taking AI from PoC through to production at scale, not just pilots.

Technical Skills (Must-Have)

• Deep TOGAF (Architecture Development Method) practice; architecture review-board governance; business value-stream mapping.
• Functional depth in at least one of SaaS platforms, fintech/payments (transactions, ledgers, identity, fraud/risk, compliance), finance automation, or Hi-Tech/semiconductor workflows — and the ability to be credible across the portfolio.
• Architectural experience with enterprise GenAI platforms/frameworks, ML model deployment, RAG pipelines, and autonomoagentic AI frameworks.
• Retains enough hands-on ability (Python, notebooks, agent/LLM SDKs, cloud services) to build quick PoCs and working prototypes independently — turning an idea into something runnable in days, not a deck.
• Cloud-native architecture across AWS/Azure/Google Cloud Platform — microservices, event-driven (Kafka), Kubernetes/Docker — and integrating AI with SaaS and enterprise platforms (Salesforce, DocuSign, payment platforms, ERP/finance, ServiceNow) via open APIs.
• Responsible-AI practices, data privacy, model/AI governance, and domain-specific compliance (e.g., PCI-DSS, SOX, data protection, fraud/resilience, IP sensitivity).
• Fluent in how AI/LLM systems are evaluated — quality/accuracy, groundedness, hallucination, safety, drift, and cost — enough to set benchmarking standards, define acceptance gates, and judge model/vendor claims. Sets the bar and governs it; does not need to hand-build every eval harness.

Technical Skills (Strong Plus)

• Real-time / low-latency decisioning and large-scale distributed systems (billions of transactions) for payments or risk platforms.
• Data architecture — lakehouse strategies, information/data modeling, streaming (Kafka/Spark), governance and consumption across domains.
• Hands-on with AI frameworks and tooling — LangChain/LlamaIndex, vector databases, MLflow, PyTorch/TensorFlow, Hugging Face.
• For Hi-Tech: familiarity with semiconductor design/manufacturing, EDA/CAD workflows, or yield/manufacturing analytics.
• Pre-sales and consulting delivery experience — proposals, estimates, and large-scale transformation programs.

Non-Technical Skills (Equally Critical)

• You simplify complex architecture for client executives, business, and technical audiences alike, present through review boards, and earn trusted-advisor status with the accounts you serve.
• Standards and frameworks are applied to deliver outcomes — not as bureaucracy. You balance competing interests of cost, complexity, and business need.
• As a principal IC you lead through architecture decisions and mentorship, not headcount — landing standards across teams you don’t manage.
• You make interoperability and build-vs-buy calls on merit, promoting open interfaces over lock-in.
• You stay ahead of a fast-moving field, form and test hypotheses rather than follow hype, and evaluate emerging models and techniques on evidence before betting the architecture on them. Comfortable with ambiguity and with being proven wrong by data.

Education

• Bachelor’s (or higher) in Computer Science / Engineering, Electrical Engineering, Applied Science, or Mathematics — or equivalent practical experience.
• TOGAF certification strongly preferred; relevant cloud architecture certifications a plus.

What Success Looks Like

• Reusable assets Published, adopted AI reference architectures, RAG/agent patterns, and accelerators reused across multiple accounts — not one-off builds.
• Governance in place A functioning review process where AI designs are assessed for standards, security, and responsible-AI/domain compliance before build.
• Production AI at scale AI/agentic capabilities grounded in client data and running in production — measurably reducing cost, risk, or cycle time, or improving experience.
• Trusted advisor Recognized by client executives as the go-to AI architecture authority; instrumental in expanding and de-risking engagements.
• PoC velocity A steady cadence of fast, credible PoCs that convert into funded, scoped engagements.
• Teams leveled up Solution architects and delivery teams operating to the standards and patterns you’ve set and mentored them on.