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About This Role
Req ID: 393533
NTT DATA strives to hire exceptional, innovative and passionate individuals who want to grow with us. If you want to be part of an inclusive, adaptable, and forward-thinking organization, apply now.
We are currently seeking a Principal AI Salesforce Architect - Remote in US to join our team in Remote, Texas (US-TX), United States (US).
Principal AI Salesforce Architect
Forward Deployed Engineering · Senior, client-facing · Full time · Regular travel to client sites
The opportunity
Agents are becoming the users of enterprise software. Salesforce is responding by moving its value beneath the screen: capabilities any agent can invoke, business logic packaged as Skills, trusted context in Data 360, orchestration across vendors in Agent Fabric, and a choice of models from Salesforce's own CRM reasoning model to the frontier labs.
Most enterprises can see the shift. Very few have someone who can explain where AI is going, decide what it means for their business and their Salesforce estate, and then make it work in production. This is a strategic, AI-forward role that stays with the client while uncertainty and risk are highest, remains through production, and hands ownership to the delivery team once the solution is proven and stable.
Not a typical FDE role
Most Forward Deployed Engineers embed one product in one company. Ours is a strategic seat in a global Salesforce practice.
- Wider. You work across industries and our most strategic clients, not inside a single deployment.
- Earlier. You shape the question with client executives before anyone designs a solution.
- Further ahead. You lead the conversation about where models, agents and Salesforce are heading, not only what ships this quarter.
- Hands-on. You prototype, debug, inspect code, understand failure modes and work directly with engineers to unblock the hardest problems.
- Lasting. What you learn in the field becomes how the whole practice sells and delivers.
What you will do
- Be the AI voice in the room. Brief client executives on where frontier and open-weight models, agents and Salesforce are heading, and translate that direction into business and technology roadmaps.
- Shape our most strategic pursuits. Frame the problem, challenge whether an agent is the right answer, set the architecture and pressure-test scope, estimates, operating assumptions and risk.
- Choose the right intelligence for each step. Decide where a frontier model, an open-weight model, a CRM reasoning model, deterministic logic or a person belongs. Weigh accuracy, cost, latency, privacy, sovereignty, hosting, operability and failure modes – not novelty.
- Design agentic systems. Architect orchestration, tool use, retrieval and grounding, memory, evaluation and guardrails across Agentforce, Data 360 and external model platforms, and define how agents are tested, monitored and improved once they are live.
- Design trusted agent authority. Define agent identity, delegated authority, tool access, action boundaries, approval checkpoints, auditability and revocation so agents act with least privilege, mapped onto the Salesforce security model and the client’s enterprise identity.
- Architect across the estate. Translate business, functional and non-functional requirements into Salesforce designs, data models and integration patterns spanning Agentforce, Data 360, MuleSoft, Databricks and the wider enterprise estate, protecting platform integrity and maintainability.
- Make it real, fast. Prototype with AI-assisted tools and our engineers; inspect and debug code, traces and integrations as needed so clients see working behavior and failure modes early, not a promise.
- Lead delivery while the risk is highest. Set direction with architects, unblock the hardest problems and lead escalations across Salesforce, MuleSoft, Data 360, models and enterprise systems, alongside delivery teams and Salesforce engineering.
- Stay through production. Remain engaged through production validation and stabilization, using real operating behavior to test the architecture and help restore stability when the hardest issues surface.
- Hand off, then move on. Once the solution is proven and stable, transition architecture decisions and the reasoning behind them, known risks, runbooks and ownership to the delivery or managed-service technical lead and take on the next frontier problem.
- Lead our thought leadership. Publish, speak and turn field patterns into the reference architectures, decision frameworks and approaches the whole practice uses.
- Raise the bar. Mentor architects and engineers on AI and agentic design so the practice’s depth grows with every engagement.
What this role is not
- Not a demo circuit. You stay with clients into production, where credibility is earned.
- Not a coding seat, and not a hands-off architect. You prototype, debug, inspect code and unblock; our engineers build and own sustained development.
- Not the delivery technical lead. You do not direct day-to-day engineering execution, run the code-review queue or own CI/CD and release management. You lead while risk is highest and then hand off.
- Not delivery management or sales. Engagement managers run delivery and sales owns the commercial outcome; you own the technical truth.
- Not a permanent fixture on one account. You go where uncertainty is highest, remain through production proof and stabilization, then transition ownership.
What you will bring
- Real AI depth. Frontier models such as Claude, GPT and Gemini, and open-weight models such as Llama, Nemotron and Mistral: their trade-offs in capability, accuracy, cost, latency, privacy, sovereignty, hosting and failure behavior, and when each belongs in an enterprise.
- Command of agentic design patterns. Tool use, planning, multi-agent orchestration, retrieval and grounding, memory, evaluation, guardrails and human oversight – including where each pattern fails in production.
- A point of view on where AI is going. And how it reshapes Salesforce: headless capabilities, Skills, trusted context, Agent Fabric, agent identity and delegated authority, and pricing that moves from seats to actions.
- Security for autonomous systems. Practical judgment in agent identity, delegated authority, least-privilege tool access, policy enforcement, human approval, auditability and revocation.
- Deep Salesforce expertise. Strong command of Salesforce architecture, security model, limits and data modeling. Real depth in at least one of Agentforce, Data 360 or MuleSoft, fluency across the rest, and the enterprise breadth to connect Salesforce to SAP, ServiceNow, Databricks and the wider data and application estate through resilient API, event and batch patterns.
- Hands-on technical fluency. Able to prototype, trace failures, inspect Apex, Lightning Web Components, Flow, APIs and integration code, read logs and work directly with engineers; you do not need to be a full-time production software engineer.
- Production scar tissue. Named enterprise AI or platform programs you took live, with specific accounts of what broke, how you diagnosed it, the trade-offs you made and how stability was restored.
- A double register. Credible with a CIO at nine and with their most skeptical architect at ten, able to disagree with either without losing the room.
- A public voice. You write and speak, and you enjoy it. You use modern AI-assisted tools every day and turn field experience into reusable guidance.
Required experience
- 10+ years delivering enterprise technology, platform or architecture programs, including substantial Salesforce experience and named deployments taken through production.
- Demonstrated enterprise AI, agentic or complex Salesforce platform delivery, with direct involvement in architecture decisions, high-risk problem solving and production stabilization.
- Evidence of operating at both executive and deep technical levels, leading through ambiguity and transitioning proven solutions to sustained delivery teams.
Preferred credentials and evidence
- Salesforce CTA, Application Architect, System Architect, Platform Developer, Agentforce, Data 360 or MuleSoft credentials are valued, but are not substitutes for production delivery and AI judgment.
- Published articles, conference presentations, reference architectures or other visible thought leadership in enterprise AI, agents or Salesforce.
You might be, today
- A Salesforce CTA or principal architect who has taken Agentforce or large platform programs live and follows the AI frontier closely.
- A Forward Deployed Engineer, applied AI engineer or senior solution engineer at Salesforce, an AI lab or a major ISV, ready for a wider canvas.
- A field CTO or principal at a systems integrator or AI-native consultancy who has put enterprise AI into production.
- An enterprise AI leader who has run agentic programs inside a large organization and now wants to shape many.
NTT DATA provides a reasonable range of compensation for U.S.-based positions. The starting pay range for this remote role is $128,858.00 - $238,625.00. This range reflects the minimum and maximum target compensation for the position across all US locations. Actual compensation will depend on a number of factors, including the candidate’s actual work location, relevant experience, technical skills, and other qualifications.
This position may also be eligible for incentive compensation based on individual and/or company performance. If the position offered in temporary, the position will not be eligible for incentive compensation.
This position is eligible for company benefits including medical, dental, and vision insurance with an employer contribution, flexible spending or health savings account, life and ADD insurance, short and long term disability coverage, paid time off, employee assistance, participation in a 401k program with company match, and additional voluntary or legally-required benefits.
About NTT DATA
NTT DATA is a $30 billion business and technology services leader, serving 75% of the Fortune Global 100. We are committed to accelerating client success and positively impacting society through responsible innovation. We are one of the world's leading AI and digital infrastructure providers, with unmatched capabilities in enterprise-scale AI, cloud, security, connectivity, data centers and application services. our consulting and Industry solutions help organizations and society move confidently and sustainably into the digital future. As a Global Top Employer, we have experts in more than 50 countries. We also offer clients access to a robust ecosystem of innovation centers as well as established and start-up partners. NTT DATA is a part of NTT Group, which invests over $3 billion each year in RD.
Whenever possible, we hire locally to NTT DATA offices or client sites. This ensures we can provide timely and effective support tailored to each client’s needs. While many positions offer remote or hybrid work options, these arrangements are subject to change based on client requirements. For employees near an NTT DATA office or client site, in-office attendance may be required for meetings or events, depending on business needs. At NTT DATA, we are committed to staying flexible and meeting the evolving needs of both our clients and employees. NTT DATA recruiters will never ask for payment or banking information and will only use @nttdata.com, @nttdatafed.com and @talent.nttdataservices.com email addresses. If you are requested to provide payment or disclose banking information, please submit a contact us form, https://us.nttdata.com/en/contact-us.
NTT DATA endeavors to make https://us.nttdata.com accessible to any and all users. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process, please contact us at https://us.nttdata.com/en/contact-us. This contact information is for accommodation requests only and cannot be used to inquire about the status of applications. NTT DATA is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status. For our EEO Policy Statement, please click here. If you'd like more information on your EEO rights under the law, please click here. For Pay Transparency information, please click here.
Salary Context
This $128K-$238K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At NTT DATA, 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($183K) sits 13% below the category median. Disclosed range: $128K to $238K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
NTT DATA AI Hiring
NTT DATA has 14 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect. Positions span Plano, TX, US, TX, US, Charlotte, NC, US. Compensation range: $125K - $372K.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% of all AI roles offer remote work.
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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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