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About This Role
Req ID: 388982
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 Senior Director of AI Partner Strategy Solutions for North America alliances to join our team in Plano, Texas (US-TX), United States (US).
Senior Director, Partner Strategy Solutions:
The Senior Director, Partner Strategy Solutions for AI leads the shaping and development of complex partner-enabled industry solutions, with particular depth in AI, generative AI, and agentic solutions. The role works from defined customer and industry problems to establish a credible solution concept, determine how NTT DATA and partner capabilities should come together, and guide the initiative through technical validation, development, packaging, and launch readiness.
This leader operates across business units, NTT DATA practices, delivery and architecture teams, engineering and product organizations, Partner Sales, Partner Execution, Marketing, and strategic technology partners. The role must be sufficiently technical to work credibly with architects, engineers, developers, and partner product teams while maintaining the commercial judgment to keep solution development anchored in buyer needs, measurable business outcomes, and a repeatable path to market.
As part of the Strategy Solutions team, the Senior Director shares responsibility for the full solution lifecycle: customer-problem intake, discovery, solution ideation, capability and gap assessment, business-case validation, development handoffs, commercialization, seller enablement, launch, and post-launch refinement. The role provides the deepest technical solutioning capability on assigned initiatives and helps establish reusable methods for developing AI-enabled and agentic offers at scale.
This is not a hands-on engineering or delivery role. Practices and delivery teams remain accountable for architecture, build, implementation, and delivery. The Senior Director ensures that technical contributors, business stakeholders, and partner teams are aligned around a solution that is feasible, differentiated, commercially viable, and ready to be packaged and activated in the market.
Location: Remote role in the United States with a preference for the Northeast, Midwest, or Southeast
Key Responsibilities
Customer problem and solution framing
- Lead discovery with business-unit, industry, sales, and client-facing stakeholders to define the customer problem, target buyer, business pressure, current-state friction, desired outcome, and reason for action.
- Determine where AI, generative AI, automation, or agentic approaches can materially improve the customer outcome rather than applying technology without a validated business need.
- Translate business problems into structured solution hypotheses, use cases, workflow concepts, capability requirements, and measurable outcome assumptions.
- Challenge weak or technology-led concepts and recommend whether an initiative should proceed, be reframed, be combined with an existing offer, or stop.
AI and agentic solution development leadership
- Shape end-to-end AI and agentic solution concepts with NTT DATA Practices and strategic technology partners, including the business workflow, human and agent roles, data dependencies, integration points, governance considerations, and expected outcomes.
- Work directly with architects, engineers, developers, product leaders, and partner technical teams to test solution feasibility, identify design tradeoffs, and ensure proposed approaches can be built, secured, operated, and delivered in an enterprise environment.
- Evaluate partner AI platforms, foundation-model capabilities, copilots, agent frameworks, orchestration technologies, accelerators, and reusable NTT DATA assets for fit within assigned solutions.
- Identify technical and capability gaps, dependencies, proof requirements, investment needs, delivery constraints, and risks early enough to influence design and governance decisions.
- Define clear development handoffs to Practices and partners, including required outcomes, acceptance criteria, decision points, technical proof points, and dependencies.
- Stay current on rapid changes in AI and agentic technologies and translate relevant developments into practical implications for solution design, differentiation, delivery, and client value.
Cross-functional solution execution
- Serve as the named Solution Development Lead for selected high-complexity solution-development initiatives and maintain accountability for progress from intake through launch readiness.
- Convene and align business units, Practices, partner teams, architecture, delivery, Marketing, Partner Sales, and Partner Execution around scope, responsibilities, milestones, decisions, and commercial outcomes.
- Lead working sessions that move ambiguous issues to decisions, clarify ownership, expose technical or commercial blockers, and maintain momentum across organizational boundaries.
- Escalate unresolved design, investment, capacity, sponsorship, or delivery issues with clear options and recommendations.
- Ensure the solution remains coherent across technical design, delivery model, partner contribution, business value, and market positioning as it progresses through development.
Validation, governance, and investment readiness
- Partner with consulting and commercial team members to develop the evidence required for investment and steering decisions, including customer need, target-account relevance, business value, technical feasibility, partner contribution, delivery readiness, differentiation, and production intent.
- Establish and validate the technical assumptions and proof points required before material development investment is approved.
- Present complex solution choices, tradeoffs, risks, and recommendations to senior business and technical stakeholders in clear, decision-ready language.
- Recommend whether initiatives should proceed, be revised, be held, or be stopped based on the combined technical and commercial evidence.
Commercialization, enablement, and launch support
- Work with Strategy Solutions colleagues to convert validated solution capabilities into a complete partner-ready offer, including use cases, buyer outcomes, differentiation, delivery approach, partner role, qualification guidance, proof points, and technical narrative.
- Ensure commercialization assets accurately represent the solution architecture, AI or agentic capabilities, deployment considerations, technical prerequisites, and client-value story without overstating maturity or feasibility.
- Enable Partner Sales Leaders and BU/Practice Sellers to understand the AI and agentic components of approved offers well enough to identify, qualify, and position opportunities credibly.
- Support launch, early pursuits, and client feedback loops to identify technical or solution changes required before broader scale.
- Capture reusable patterns, reference designs, discovery methods, qualification criteria, and lessons learned so subsequent solution-development efforts become faster and more repeatable.
Team capability and thought leadership
- Coach other Strategy Solutions team members in AI and agentic solution development, technical discovery, solution qualification, and effective collaboration with Practice and partner technical teams.
- Create practical frameworks, templates, and quality standards that raise the technical fluency and solution-development discipline of the broader team.
- Represent Strategy Solutions with senior technical, business, and partner stakeholders on priority AI and agentic initiatives.
Key Interfaces
- Managing Director, Strategy Solutions
- Strategy Solutions team members and Solution Development Leads
- Partner Sales Leaders
- Partner Execution
- Business-unit leaders and BU/Practice Sellers
- Industry, practice, solution, delivery, architecture, engineering, and product leaders
- Marketing and sales-enablement teams
- Strategic technology-partner product, technical, sales, and go-to-market teams
- Global partner and solution stakeholders
- Steering-committee and executive-governance participants
Success Measures
- Quality, feasibility, and differentiation of AI-enabled and agentic solutions progressed through the lifecycle
- Successful conversion of validated customer problems into buildable, partner-enabled solution concepts
- Practice, delivery, architecture, partner, and business-unit commitment to assigned solutions
- Quality and timeliness of technical development handoffs, decisions, proof points, and risk identification
- Readiness of approved solutions for commercialization, seller enablement, and market launch
- Qualified pipeline, production deployments, reusable assets, and referenceable client outcomes associated with assigned solutions
- Ability of the broader Strategy Solutions team to apply repeatable AI and agentic solution-development methods
- Disciplined correction or retirement of initiatives that lack technical feasibility, customer value, sponsorship, or commercial traction
The role does not own hands-on engineering, underlying technology development, delivery execution, account ownership, a direct sales quota, partner-sales execution, deal-registration processing, or Salesforce opportunity ownership.
Required Qualifications
- Demonstrated success shaping, packaging, launching, and scaling enterprise AI, generative-AI, or agentic solutions into the market; evidence of successful launches and real client or production outcomes is required.
- Significant experience in AI solution development, technology consulting, solution architecture, product or offer development, digital transformation, industry solutions, or a closely related field.
- Strong working knowledge of modern AI solution patterns, including large language models, agentic workflows, retrieval and grounding approaches, APIs and integrations, data dependencies, evaluation, security, governance, and enterprise deployment considerations.
- Hands-on proficiency using leading large language models and generative-AI tools as part of day-to-day work, including prompt design, research, synthesis, analysis, rapid prototyping, content development, and output validation.
- Ability to engage credibly with software engineers, architects, developers, product leaders, delivery teams, and partner technical organizations without needing to be the hands-on builder.
- Strong commercial judgment and demonstrated ability to connect technical capabilities to customer problems, buyer value, business outcomes, and repeatable market propositions.
- Proven ability to lead complex cross-functional initiatives and influence senior business and technical stakeholders without direct authority over all contributors.
- Experience with strategic technology-partner ecosystems, enterprise technology services, and go-to-market motions strongly preferred.
- Excellent executive communication, facilitation, decision support, and problem-solving capabilities.
- Bachelor’s degree required; advanced degree or relevant technical, AI, product, or business credentials preferred.
Basic Required Qualifications:
- 6 + years experience shaping, packaging, launching, and scaling enterprise AI, generative-AI, or agentic solutions into the market; evidence of successful launches and real client or production outcomes is required.
- 8+ years of technology consulting experinece and solution development including AI
Core Competencies
- AI and agentic solution fluency
- Technical-to-business translation
- Solution-development judgment
- Enterprise commercial orientation
- Cross-functional leadership
- Executive influence
- Decision-making under ambiguity
- Technology and market curiosity
- Risk and dependency management
- Coaching and capability building
- Accountability for outcomes
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.
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 9,653 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 $212,000 based on 11,857 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
NTT DATA AI Hiring
NTT DATA has 7 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect. Positions span Plano, TX, US, London Area, United Kingdom, Atlanta, GA, US. Compensation range: $145K - $372K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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