Technical Architect

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Job Category

Enterprise Technology & Infrastructure

Job Details

About Salesforce

Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn’t a buzzword — it’s a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all.

Ready to level-up your career at the company leading workforce transformation in the agentic era? You’re in the right place! Agentforce is the future of AI, and you are the future of Salesforce.

Digital Technology blazes the trail of enterprise IT at Salesforce. Grounded in our core values; Trust, Customer Success, Innovation, and Equality (Ohana); we do far more than run traditional IT. We co-create with our business partners to deliver scalable, secure, AI-powered, and revenue-critical capabilities on the world’s foremost Salesforce implementation (Salesforce on Salesforce). DET spans technology strategy, Salesforce on Salesforce, applications engineering, data & AI platforms, integration, collaboration, enterprise operations, architecture & governance, and program enablement.

We’re looking for a Technical Architect to design, govern, and guide delivery of cloud-native, Salesforce-first, AI-enabled solutions across our Revenue (Q2C) and eCommerce landscape. You will be the technical authority partnering with Solution Architects, Product Owners, Engineers, and Business leaders to ensure architectures are secure, performant, observable, and cost-efficient; and that they align to DET’s target state architecture, standards, and roadmap.
This role requires deep hands-on expertise with the Salesforce platform (RLM/CPQ, Sales/Service/Experience/Commerce Clouds, Data Cloud, Einstein/Agentforce), API & event-driven integration (MuleSoft, Kafka), modern front-end patterns, and GenAI/ML enablement, coupled with the ability to mentor engineers, codify best practices, and influence at scale. We are also looking for strong experience in building and architecting AWS services to support scalable and reliable enterprise applications.

What You’ll Do
1) Architecture, Design & Delivery

• Own end-to-end technical architecture for large, multi-cloud Salesforce initiatives spanning Quote-to-Cash, Revenue Lifecycle Management (RLM), Order Management, Contracts & Assets, Pricing, Approvals, DocGen/eSign, and Internationalization/Multi‑Currency.
• Translate business capabilities into current/future-state architectures, reference architectures, sequence diagrams, integration contracts, and non-functional requirements (availability, performance, DR, observability, compliance).
• Drive API-first and event-driven patterns using MuleSoft, Kafka/EventBridge (or equivalent), platform events, Change Data Capture, and well-defined domain boundaries.
• Champion secure-by-design principles: SSO, OAuth/JWT, data classification & encryption, Shield, key management, and zero-trust patterns.
• Design and implement scalable AWS services including Lambda, S3, ECS, API Gateway, CloudFormation, and more to support extensible and cloud-native architecture patterns.

2) Q2C, Revenue & eCommerce Domain Expertise

• Architect scalable, configurable solutions across RLM / CPQ, Order Management, Billing/ERP integrations, Revenue Recognition, and pricing/discounting engines.
• Ensure seamless data & process orchestration between Salesforce, ERP, billing, finance, and eCommerce platforms (Salesforce B2C/B2B Commerce, headless APIs, composable storefronts).
• Establish guardrails for customization vs. product capability (RLM/RLM rules, managed packages) and create roadmaps to move customizations to product-first approaches.

3) AI / GenAI & Data

• Partner with the AI/Architecture councils to embed AI responsibly: leverage Einstein, Agentforce, LLMs (RAG, prompt engineering, safety/guardrails), and Data Cloud for segmentation, personalization, forecasting, and agentic automation.
• Define ML/GenAI architectural patterns (training, inference, feature stores, observability, bias & drift monitoring) and integration to our Trust Layer, governance, and compliance frameworks.
• Evaluate model/platform choices (open/closed-source LLMs, Vertex/SageMaker, internal platforms) against cost, latency, scale, privacy, and IP protection.