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About This Role
Description
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Director, Technical Architects \- Data and AI
### 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.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Informatica, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $166,983 based on 13,781 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 Architect ($292,900). 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/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
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).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
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.
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