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Job Category
Sales
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.
Director, Technical Architects \- Data and AI
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.
Department Description
==========================
The Data \& AI Expert team is an innovative group of technical architects at the heart of Salesforce's newest innovations — Data360 and Agentforce. They don't just work with today's technology — they help customers architect for the future. This is a startup within a massive organization, focused on next\-generation Data \+ AI \+ CRM \+ Analytics at Salesforce.
This team sits at the center of our GTM strategy, connecting the dots between Data \& AI Products, Product Marketing, Enablement, Customer Success \& Support, and the Partner Ecosystem. But their impact doesn't stop at the sale — this team operates across the full customer lifecycle, ensuring customers don't just buy Data360 and Agentforce, but genuinely succeed with it.
Your Impact
===============
As a leader, your primary impact is building elite Data \& AI Technical Architects and enabling them to operate at scale — across the full customer journey, from initial discovery through to realized value.
- Build and lead a best\-in\-class team of Data and AI Technical Architects, recognized for deep expertise across data architecture, AI, Salesforce, and enterprise integration patterns.
- Recruit and mentor senior\-level architects with versatile communication styles. Our goal is to build a team capable of translating technical architecture into business value, whether they are whiteboarding with engineers or securing buy\-in from CDOs and CIOs.
- Establish clear technical standards, architectural principles, and operating rhythms that ensure consistent, high\-quality execution across the territory.
- Partner with Sales Leadership to define coverage and engagement models that align technical architect capacity to the highest\-impact accounts and opportunities.
- Serve as a technical executive sponsor for strategic customers — guiding enterprise data and AI strategy, influencing architectural decisions, and earning long\-term trust that extends well beyond the close.
- Champion customer outcomes across the full lifecycle — partnering with Customer Success, Support, and Professional Services to ensure technical wins translate into measurable business value. The greatest impact is realized when technical leadership remains a constant partner from the initial sale through to final delivery.
- Design and scale repeatable technical evaluation motions — architectural workshops, POVs, POCs, and reference designs — that accelerate time\-to\-value for Data and AI initiatives.
- Drive strong alignment with Product and Engineering to stay ahead of the Data \& AI roadmap and represent field\-driven architectural requirements.
- Work closely with the Partner Ecosystem to develop offerings, use cases, and GTM strategies that extend Salesforce's Data360 reach.
- Develop scalable, reusable technical enablement assets that uplift the architectural maturity of Solution Engineers, sellers, partners, and customers.
- Own Salesforce's point\-of\-view on enterprise data and AI architecture, aligned to industry\-specific needs and grounded in proven, repeatable patterns.
- Bring a futurist mindset — anticipating where data, AI, and CRM technology is heading and helping customers, your team, and Salesforce think 2\-3 years ahead, not just solve for today's problems.
- Drive operational rigor and accountability, linking architectural excellence to ACV growth, expansion, and customer success.
Qualifications
==================
You are a people builder and architecture leader, with a proven ability to scale deep technical talent:
- 10\+ years leading Solution Engineering, Technical Architecture, or Technical Sales teams in Data, Analytics, and AI domains.
- Demonstrated success building and scaling teams of senior architects.
- BS in Computer Science, Engineering, Data Science, or equivalent experience; advanced degree preferred.
- Strong full\-stack technical credibility across enterprise data platforms, AI/ML concepts, and Salesforce architecture — including hands\-on familiarity with Agentforce, Data360, and the Salesforce platform.
- Working knowledge of agent interoperability standards (e.g., MCP, A2A) and the broader external agent ecosystem — enough to coach architects and shape Salesforce's architectural point\-of\-view.
- Understanding of prompt engineering, agent lifecycle strategy, and applied Generative AI — able to set standards and review the work of senior architects in these areas.
- Proven ability to engage and influence C\-level executives — including CDOs and CIOs — while remaining grounded in hands\-on architectural realities.
- Data\-driven leader with a history of defining and over\-achieving on technical KPIs, such as architectural win rates, technical debt reduction, and time\-to\-value for AI deployments.
- Experience driving success within large, matrixed enterprise sales organizations.
- Track record of leadership recognition, customer impact, and measurable business outcomes.
- Deep understanding of industry\-specific data challenges, enterprise data governance (e.g., Informatica, Collibra), architectural patterns, and complex integration strategies.
- Exceptional communicator and storyteller — able to translate complex Data and AI architectures into executive\-level value narratives.
- Familiarity with the Partner Ecosystem and experience co\-developing GTM strategies with SI and ISV partners.
Technical Skill Set
=======================
This leader sets the architectural standard and is expected to remain close enough to the work to coach, review, and challenge designs:
Data Architecture \& Platforms
----------------------------------
Cloud data platforms, SQL, ingestion pipelines (batch, streaming, CDC), ETL/ELT, data modeling, semantic layers, integration patterns, and the TCO tradeoffs of building with hyperscalers vs. Salesforce. Deep familiarity with Informatica (IDMC) for enterprise data integration and MDM, alongside Snowflake, Databricks, BigQuery, and Redshift.
AI \& Analytics
-------------------
Foundational ML concepts, data preparation, Python/R, applied AI, Generative AI, prompt engineering, and agentic use cases. Hands\-on familiarity with platforms like Databricks, Snowflake, Sagemaker, or Vertex is a strong plus. Awareness of agent lifecycle management, observability, and governance frameworks.
Agentforce \& Salesforce Platform
-------------------------------------
Admin\-level fluency, hands\-on experience across Salesforce Clouds, and deep architectural understanding of how Data and AI are activated within Salesforce — including Agentforce, Data360, Sales Cloud, Service Cloud, and Industry Clouds. Understanding of how external agents (Copilot, Gemini, etc.) integrate with Agentforce via open standards.
Analytics \& BI
-------------------
Working knowledge of tools like Tableau, PowerBI, or Looker and how they connect to enterprise data strategy.
Integration Patterns
------------------------
MuleSoft APIs, hyperscaler SDKs, cross\-platform integrations, and enterprise governance and compliance frameworks.
Key Qualities
=================
- Futurist: Sees beyond today's problems — consistently brings a forward\-looking perspective to customers, the team, and the product roadmap. Connects today's architectural decisions to tomorrow's possibilities.
- Passion: Deeply committed to customer success — before, during, and after the sale.
- Leadership: Self\-aware, strategic thinker, skilled at building relationships and developing people.
- Communication: Communicates with clarity, brevity, and purpose; influences and engages at all levels.
- Storytelling: Translates complex technical concepts into compelling narratives that resonate with both technical and business audiences.
- Adaptability: Thrives in high levels of uncertainty and change — comfortable being a pioneer.
- Horizon Thinker: Doesn't just respond to the market — helps shape it.
- Trust: Demonstrates integrity, transparency, and reliability with customers, teammates, and partners.
- Community Champion: Actively contributes to the knowledge base within the organization and the broader ecosystem.
- Curiosity: Stays close to the technology — continuously learning, experimenting, and bringing new ideas back to the team.
Unleash Your Potential
When you join Salesforce, you’ll be limitless in all areas of your life. Our benefits and resources support you to find balance and *be your best* , and our AI agents accelerate your impact so you can *do your best* . Together, we’ll bring the power of Agentforce to organizations of all sizes and deliver amazing experiences that customers love. Apply today to not only shape the future — but to redefine what’s possible — for yourself, for AI, and the world.
Accommodations
If you need a reasonable accommodation during the application or the recruiting process, please submit a request via this Accommodations Request Form .
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Posting Statement
Salesforce is an equal opportunity employer and maintains a policy of non\-discrimination with all employees and applicants for employment. What does that mean exactly? It means that at Salesforce, we believe in equality for all. And we believe we can lead the path to equality in part by creating a workplace that’s inclusive, and free from discrimination. Know your rights: workplace discrimination is illegal. Any employee or potential employee will be assessed on the basis of merit, competence and qualifications – without regard to race, religion, color, national origin, sex, sexual orientation, gender expression or identity, transgender status, age, disability, veteran or marital status, political viewpoint, or other classifications protected by law. This policy applies to current and prospective employees, no matter where they are in their Salesforce employment journey. It also applies to recruiting, hiring, job assignment, compensation, promotion, benefits, training, assessment of job performance, discipline, termination, and everything in between. Recruiting, hiring, and promotion decisions at Salesforce are fair and based on merit. The same goes for compensation, benefits, promotions, transfers, reduction in workforce, recall, training, and education.
In the United States, compensation offered will be determined by factors such as location, job level, job\-related knowledge, skills, and experience. Certain roles may be eligible for incentive compensation, equity, and benefits. Salesforce offers a variety of benefits to help you live well including: time off programs, medical, dental, vision, mental health support, paid parental leave, life and disability insurance, 401(k), and an employee stock purchasing program. More details about company benefits can be found at the following link: https://www.salesforcebenefits.com.
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 26,159 AI roles we're tracking, AI Architect positions make up 1% of the market. At Salesforce, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Architect roles pay a median of $292,900 based on 108 positions with disclosed compensation. Director-level AI roles across all categories have a median of $244,288.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Salesforce AI Hiring
Salesforce has 19 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect, AI Software Engineer. Positions span San Francisco, CA, US, New York, NY, US, Chicago, IL, US. Compensation range: $155K - $451K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 median).
Career Path
Common paths into AI Architect roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 roles).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (2,416) are outnumbered by mid-level (16,247) and senior (5,153) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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