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About This Role
Description
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The Data \& AI Architecture team is an innovative group of technical architects at the heart of Salesforce's newest innovations: Data360 and Agentforce. They do not just work with today's technology. They help customers architect for what comes next. 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 does not stop at the sale. This team operates across the full customer lifecycle, helping ensure customers do not just buy Data360 and Agentforce, but genuinely succeed with them.
### 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.
This role is not about managing product specialists. It is about building a team of trusted technical architects who can create leverage, redesign workflows, and shape enterprise data and AI strategy.
- Build and lead a best\-in\-class team of Data \& 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 so technical wins translate into measurable business value and durable adoption.
- Design and scale repeatable technical evaluation motions, including 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 assets, patterns, and enablement that raise 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.
- Think beyond today's roadmap. Anticipate how data, AI, and CRM architectures are evolving, and help customers, your team, and Salesforce prepare for the next operating model, not just the next release.
- Build a team that scales through reusable patterns, AI\-native workflows, and strong operating discipline. Hiring more people is not the default answer. Building more leverage is.
- 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 in Solution Engineering, Technical Architecture, Technical Sales, or related technical GTM leadership across Data, Analytics, and AI domains.
- Demonstrated success hiring, developing, and scaling teams of senior technical architects or equivalent customer\-facing technical leaders.
- BS in Computer Science, Engineering, Data Science, or equivalent experience; advanced degree preferred.
- Strong technical credibility across enterprise data platforms, AI/ML concepts, integration patterns, and Salesforce architecture, including practical familiarity with Agentforce, Data360, and the broader Salesforce platform.
- Working knowledge of agent interoperability standards, for example MCP and A2A, and the broader external agent ecosystem, enough to coach architects, evaluate new patterns, and shape Salesforce's architectural point of view.
- Understanding of agent design, prompt and instruction patterns, evaluation strategy, and applied Generative AI, enough to set standards and review the work of senior architects in these areas.
- Proven ability to engage and influence C\-level executives, including CDOs, CIOs, and CTOs, while remaining grounded in hands\-on architectural realities.
- Data\-driven leader with a history of defining and outperforming against technical GTM metrics such as architectural win rates, attach influence, reusable asset adoption, and time\-to\-value for AI and data initiatives.
- Experience driving success inside large, matrixed enterprise sales organizations.
- Track record of leadership recognition, customer impact, and measurable business outcomes.
- Deep understanding of enterprise data governance, industry\-specific data challenges, modern architectural patterns, and complex integration strategies across heterogeneous environments. Familiarity with platforms such as Informatica or Collibra is a plus.
- 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.
- Ability to scale impact through reusable patterns, AI\-native workflows, and strong operating discipline, not just by adding headcount.
### 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: Strong grounding in cloud data platforms, ingestion patterns (batch, streaming, CDC), ETL / ELT, data modeling, semantic layers, and the architectural tradeoffs of building with hyperscalers versus Salesforce. Familiarity with enterprise data integration and governance patterns, including platforms such as Informatica, Snowflake, Databricks, BigQuery, and Redshift.
- AI \& Analytics: Strong understanding of foundational ML concepts, data preparation, applied AI, Generative AI, and agent\-based use cases. Familiarity with evaluation, observability, governance, and lifecycle management for AI systems. Hands\-on experience with platforms such as Databricks, Snowflake, SageMaker, or Vertex is a strong plus.
- Agentforce \& Salesforce Platform: Admin\-level fluency and strong architectural understanding of how data and AI are activated inside Salesforce, including Agentforce, Data360, core CRM clouds, and industry\-specific workflows. Understanding of how external agents and copilots integrate with Salesforce through open standards and interoperable architectures.
- Analytics \& BI: Working knowledge of tools such as Tableau, Power BI, or Looker, and how analytics, semantic layers, and reporting fit into enterprise data strategy and activation.
- Integration Patterns: Strong familiarity with APIs, event\-driven architectures, MuleSoft, hyperscaler services, and cross\-platform integration patterns, along with the governance and compliance considerations required in enterprise environments.
### Key Qualities
- Systems Thinker: Connects today's architecture decisions to future operating models, organizational design, and customer outcomes.
- Customer Ownership: Deeply committed to customer success before, during, and after the sale.
- Leadership: Self\-aware, strategic, and skilled at building relationships, trust, and talent.
- Communication: Communicates with clarity, brevity, and purpose; influences effectively at every level.
- Storytelling: Translates complex technical concepts into compelling narratives for both technical and business audiences.
- Adaptability: Thrives in uncertainty and change; comfortable building in ambiguous, pioneering environments.
- Trust: Demonstrates integrity, transparency, and reliability with customers, teammates, and partners.
- Force Multiplier: Creates reusable knowledge, patterns, and assets that raise the capability of the broader organization.
- Curiosity: Stays close to the technology, continuously learning, experimenting, and bringing back better ideas.
For roles in San Francisco and Los Angeles: Pursuant to the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, Salesforce will consider for employment qualified applicants with arrest and conviction records.
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 Informatica, 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.
Informatica AI Hiring
Informatica has 39 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer, AI Architect, AI Software Engineer. Positions span IN, US, CA, US, TX, US.
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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