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About This Role
*To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts.*
Job Category
Customer Success
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.
Applications will be accepted until 06/30/2026\.
The Senior AI and Data Architect is a recognized expert and demonstrated thought leader in the technical and functional application of Agentforce for enterprise customer engagements. This role significantly influences the successful architecture and delivery of complex Agentforce projects and drives internal knowledge scaling.
This position sits within CSG Professional Services, on the Data Excellence team. Its functions are split equally between delivery and internal employee enablement, whereby half of the role is customer\-facing and involves serving Salesforce’s enterprise Agentforce customers as a technical advisor, and the other half is spent on enabling CSG employees on Agentforce by providing technical expertise; documenting reference architecture; building reusable tools, assets, and patterns; running live enablement sessions; and providing from\-the\-field feedback (Voice of the Customer) to the Product and Engineering organizations to contribute to product enhancements.
Key Responsibilities* Serve as the go\-to subject matter expert on Salesforce Agentforce for internal teams and enterprise customers, providing deep insight into its capabilities, technical architecture, potential applications, and crucial limitations. Advise definitively on optimal use cases, implementation patterns, and when alternative or complementary solutions are more appropriate for a given use case.
- Act as an escalation point for critical or complex Agentforce project challenges, providing expert diagnosis, recommending strategic and technical solutions, and guiding teams toward successful resolution.
- Lead the technical design and hands\-on development of complex Agentforce solutions, including creating and editing agents, prompts, topics, actions, flows, and writing necessary Apex and SOQL code to meet customer requirements.
- Partner with customers and account teams to assess business challenges, identify high\-impact Agentforce use cases, define solution scope, develop detailed technical designs, and articulate the business value of proposed solutions to foster successful adoption and identify future opportunities.
- Lead and contribute to mentorship and enablement programs, developing and delivering technical content, documenting reference architectures, building reusable assets and patterns, and running internal training sessions to scale Agentforce knowledge within CSG.
- Maintain a high degree of expertise on the rapidly evolving Agentforce product roadmap and the broader generative AI landscape through continuous self\-directed learning.
- Provide critical “Voice of the Customer” feedback to Product and Engineering organizations based on field experience, directly contributing to product enhancements and future features.
- Actively partner with GTM counterparts during pre\-sales activities by providing expert technical validation, shaping scope, and contributing to the development of future Services offerings related to Agentforce and AI.
Manage parallel engagements across multiple (typically 2\-3\) strategic clients simultaneously, meeting a utilization target of 50%.
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Qualifications and Skills* Practical, hands\-on, real\-life Agentforce experience guiding customers on the use of the technology.
- Salesforce AI Specialist certification and Agentblazer Innovator status is required.
- Experience with AI\-related data integration technologies and concepts, including RAG, vector databases, search indexes, and knowledge bases, specifically how they apply to grounding Agentforce solutions with enterprise data.
- Strong understanding of data management concepts for structured and unstructured data within the Salesforce ecosystem (including Salesforce CRM and Data Cloud) and external systems, including data integration, transformation (ETL/ELT), and data governance considerations.
- Comprehensive and current knowledge of the rapidly evolving LLM (Large Language Model) landscape, including understanding the specifications, strengths, weaknesses, and ideal application scenarios of major foundational models, the tooling ecosystem, and the state of frontier models relevant to enterprise AI deployments.
- Relevant Salesforce experience in Sales Cloud, Service Cloud, and Data Cloud, and related certifications (Salesforce Administrator, Service Cloud Consultant, Sales Cloud Consultant, Data Cloud Consultant) are a strong plus.
- Strong aptitude toward communicating complex business and technical concepts using visualization and modeling aids, and the ability to conceptualize and create sophisticated diagrams and documents.
- Knowledge of Data Governance, Data Security, and Data Privacy concepts and regulations is preferred.
- Salesforce Data Cloud Consultant certification is preferred.
- BA/BS degree or foreign equivalent in a technical or related field.
Willingness to travel when needed (expected to be less than 10%).
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Required Qualities* PASSION: Passionate about Customer Success.
- BEGINNER’S MIND: Always learning; approaches each interaction with open mind; great listener and hands\-on.
- LEADERSHIP: Self\-aware and strategic thinker; proficient at building strong relationships.
- COMMUNICATOR: Speaks and writes with clarity, brevity, and purpose; explains area of expertise clearly and confidently to others; influences and engages C\-Level with authority and confidence.
- STORYTELLER: Confidently and effectively facilitates and presents; ably defends point\-of\-view; keeps audiences engaged and delivers a clear and memorable message.
- TEAM PLAYER: Proficient at collaboration and working with members of a team.
- URGENCY: Ability to move fast and drive business value and results.
- TRUST: Trusts the company’s core values; shows integrity, transparency, and reliability.
- ADAPTABLE: Excels in high levels of uncertainty and change.
- COMMUNITY CHAMPION: Leads internal initiatives; actively contributes to the Community’s knowledge and resource base.
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 .
Please note that Salesforce uses artificial intelligence (AI) tools to help our recruiters assess and evaluate candidates’ resumes and qualifications throughout the recruiting process. Humans will always make any candidate selection and hiring decisions. Please see our Candidate Privacy Statement for more information about how we use your personal data and your rights, including with regard to use of AI tools and opt out options.
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.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.
At Salesforce, we believe in equitable compensation practices that reflect the dynamic nature of labor markets across various regions.\&\#xa;\&\#xa;The typical base salary range for this position is $150,100 \- $227,000 annually. Your recruiter can share more about the specific salary range for the job location during the hiring process.\&\#xa;\&\#xa;There is a different range applicable to specific work locations. In California and New York, and select cities in the metropolitan areas of Boston, Chicago, Seattle, and Washington DC, the base pay range for this role in those locations is $180,200 \- $247,900 per year. Your recruiter can share more about the specific salary range for the job location during the hiring process.\&\#xa;\&\#xa;The range represents base salary only, and does not include company bonus, incentive for sales roles, equity or benefits, as applicable.
Salary Context
This $150K-$247K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Salesforce, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($199K) sits 10% above the category median. Disclosed range: $150K to $247K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Salesforce AI Hiring
Salesforce has 11 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, Research Engineer, AI Product Manager. Positions span Bellevue, WA, US, San Francisco, CA, US, Palo Alto, CA, US. Compensation range: $223K - $344K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 median, while Prompt Engineer roles sit at $140,000. 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: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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