Pre-Sales Data and AI Architect - Solution Engineering (United States)

Salesforce•Boston, MA

6d•$148,190 - $255,150•Remote

About The Position

Salesforce is seeking a Pre-Sales Data and AI Architect to join their Solution Engineering team. This role focuses on helping customers understand and implement the possibilities of the new AI era, focusing on architecture, data foundation, operating models, and adoption. The architect will be a thought leader and field specialist for customers using Salesforce Data 360, Agentforce, and the broader enterprise data and AI ecosystem. The position requires a blend of technical architecture, solution engineering, applied data and AI, and delivery-aware judgment to translate customer ambitions into practical, actionable architectures. The role demands both deep expertise in trusted data and agent foundations, and broad understanding of the customer's ecosystem. Key responsibilities include sharing a point of view on enterprise data and AI, understanding Salesforce data & AI products, partnering with sales teams, leading customer architectural discussions, building customer confidence through demos and prototypes, and continuously learning about emerging technologies. The architect will also be responsible for turning field learnings into reusable assets and product feedback.

Requirements

7+ years experience in technical architecture, enterprise architecture, solution engineering, consulting, implementation, software engineering, product development, or related work in complex enterprise environments.
A track record of shaping technical strategy with input from customers and /or stakeholders, validating solution approaches, and connecting business needs to technology decisions.
Strong understanding of enterprise data, AI, integration, governance, security, privacy, and platform architecture concepts and patterns.
Experience working across Sales, Product, Engineering, Support, and delivery or customer teams to move solutions from idea to execution.
The ability to distill and communicate complex technical ideas to executive and technical audiences.
Demonstrated curiosity and learning velocity in emerging technologies — especially within the AI, data, automation, and agentic systems space.
Bachelor’s degree in Computer Science, Software Engineering, Electrical Engineering, Data Science, other STEM degrees or equivalent work experience. Graduate study a plus.

Nice To Haves

Salesforce Data 360 (Data Cloud), Agentforce, MuleSoft, Tableau, Informatica, or the broader Salesforce Platform.
Conversational AI, voice-enabled AI agents, real-time voice orchestration, telephony integrations, or multi-channel service experiences.
Modern data platforms such as Snowflake, Databricks, BigQuery, or RedShift, and the hyperscaler ecosystems around them.
AI agents, generative AI, AI governance and observability, external agent ecosystems, hyperscaler AI services, or enterprise automation.
Programming languages such as Python, common data structures and tooling, and AI-assisted development pipelines.
Building demos, prototypes, proofs of concept, workshops, reference architectures, or field enablement assets.

Responsibilities

Share a clear point of view on enterprise data and AI — how agents, data platforms, CRM, trust, identity, workflow, governance, and adoption come together into something that delivers value through Salesforce.
Deeply understand our data & AI products and articulate the value of the Salesforce platform.
Partner with sellers and solution teams to uncover customers' real problems, shape technical strategy, and pressure-test feasibility and cost-to-serve early.
Lead customer architectural discussions around Data 360, Agentforce, data structures and patterns, AI readiness, identity management, governance, integration, automation, and business value.
Build customer confidence through demos, prototypes, proofs of concept, reference patterns, and architectural walkthroughs that demonstrate what's real as well as what it will take to operationalize and scale.
Continuously scan the horizon and tinker to refine your judgement on what's worth building and how to build within the data and AI space.
Turn what you learn in the field into reusable assets, enablement, best practices, and product feedback that extend your impact beyond your immediate teams and stakeholders.