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Make an impact at NTT Global Data Centers
Join NTT Global Data Centers and be part of a team that drives innovation and sustainability in the digital world. With over 150 data centers across more than 20 countries globally, we offer unparalleled opportunities to work on cutting\-edge technology and transformative projects. Experience a collaborative, innovative, and inclusive workplace where your ideas are valued, and your growth is supported.
Your role at a glance
The Principal AI Security \& Privacy Architect owns the security and privacy architecture for AI/ML\-enabled products and enterprise AI platforms. This role partners across the enterprise to embed secure\-by\-design and privacy\-by\-design controls across the AI lifecycle — delivering trustworthy, compliant, audit\-ready AI solutions, tailored to specific business needs. The ideal candidate brings a rigorous engineering foundation, deep governance experience, and the project leadership skills to drive cross\-functional initiatives from scoping through delivery.
What we are looking for
Key Roles and Responsibilities:
AI Architectures
- Train the entire business on safe AI usage that adheres to privacy and cybersecurity goals.
- Coordinate with parent company on AI solutions, vetting strategies, security and privacy governance and guidance.
- Design solutions for LLM usage and AI\-generated software that can be easily adopted by the business.
- Analyze and support proposed AI projects companywide, serving as the technical reviewer for the AI Center of Excellence.
AI Security, Privacy \& Governance
- Architect security and privacy controls for AI/ML solutions, including data and model pipelines, AI\-enabled product features, cloud services, and customer\-facing surfaces.
- Define secure\-by\-design and privacy\-by\-design patterns; translate regulatory and policy requirements (GDPR, DPDPA 2023, NIST AI RMF, ISO 42001\) into actionable engineering guidance.
- Lead hands\-on PIA, DPIA, and TPRA assessments for AI/ML systems and data platforms, identifying risks including re\-identification, inference attacks, model inversion, and consent gaps.
- Review AI model cards, training data sourcing practices, and automated decision\-making workflows to surface privacy, fairness, and transparency risks.
- Serve as a subject\-matter expert on privacy\-by\-design principles as applied to generative AI, LLMs, recommendation systems, and other deployed AI tools.
Risk Management \& Assurance
- Define AI risk and control frameworks aligned to the enterprise risk model; drive mitigations, documentation standards, and risk acceptance packages.
- Support the risk register and compliance calendar for AI security and privacy activities, tracking open issues, remediation actions, and regulatory deadlines.
- Prepare and present governance status reports, risk summaries, and architectural decision records to senior leadership and relevant committees.
- Support vendor and third\-party AI tool assessments, including contractual data processing reviews, security questionnaires, and due diligence activities.
Project Management \& Cross\-Functional Leadership
- Lead end\-to\-end governance and architecture projects — from scoping and stakeholder alignment through implementation and post\-deployment review — on time and within defined parameters.
- Integrate AI security and privacy requirements into the secure SDLC, architecture review boards, go\-to\-market readiness gates, and customer/regulatory assurance responses.
- Coordinate with Engineering, Data Science, Legal, Product, and Compliance teams to embed governance checkpoints throughout the AI/ML development lifecycle.
- Manage project plans, milestone tracking, and status reporting using tools such as Jira, Asana, or MS Project; proactively surface blockers and drive resolutions across workstreams.
- Facilitate architecture and risk review sessions, documenting decisions and ensuring follow\-through on action items with accountable owners and defined timelines.
KNOWLEDGE \& ATTRIBUTES
- Deep analytical and regulatory interpretation skills with the ability to translate complex AI risk requirements into practical, implementable guidance for engineering teams.
- Working knowledge of AI/ML concepts: supervised and unsupervised learning, generative AI, LLMs, automated decision\-making, and model governance.
- Structured, methodical approach to project management — experienced with agile and waterfall delivery frameworks, comfortable with ambiguity, and adept at prioritizing competing demands across concurrent workstreams.
- Excellent written and verbal communication skills; ability to present complex security and risk topics credibly to both technical and executive audiences.
- Collaborative, curious, and proactive — a self\-starter who builds trust with cross\-functional peers and drives initiatives through to measurable outcomes.
ACADEMIC QUALIFICATIONS \& CERTIFICATIONS
- Bachelor’s degree required — preferably in Computer Science, Information Security, Law, Privacy, Engineering, Governance/Risk Management/Compliance, or related discipline.
Certifications — Desired (any of the following)
- CIPP/E, CIPP/A, CIPM, or CIPT (IAPP) — Data Privacy
- AI Governance Professional (AIGP) (IAPP) — AI \& Privacy combined
- CISSP, CCSP, or equivalent — Information / Cloud Security
- ISO 42001 Lead Implementer or Auditor — AI Management Systems
REQUIRED EXPERIENCE
- Senior\-level experience (7\+ years) in privacy, security, and product architecture with accountability for governance and risk decisions.
- Demonstrated hands\-on leadership of PIA/DPIA/TPRA and privacy\-by\-design assessments for AI/ML systems and enterprise data platforms.
- Strong security engineering foundation including secure SDLC, application and product security, and threat and risk assessment methodologies.
- Proven track record leading cross\-functional projects with multiple stakeholders; familiarity with project management tools (e.g., Jira, Asana, MS Project) is required.
Preferred Qualifications
- Experience supporting enterprise AI governance programs and/or contributing to AI policy development at a senior level.
- Familiarity with AI/ML model governance tooling, MLOps pipelines, and data lineage practices.
- Experience working within regulated industries (financial services, healthcare, or technology) where AI assurance is subject to external audit.
PHYSICAL REQUIREMENTS
- Primarily sitting with some walking, standing, and bending.
- Able to hear and speak on the telephone.
- Close visual work on a computer terminal.
- Dexterity of hands and fingers to operate any required to operate computer keyboard, mouse, and other technical instruments.
Work conditions \& other requirements
- Extensive daily usage of workstation or computer.
- Must be available for 24x7 support of customers and NTT GDC.
- Must be reachable via telephone, text, and email on a 24x7 basis.
- Some travel as required. Travel requirements may vary based on the frequency and intensity of remote site operations
UID: 3023
Compensation Pay Range
For roles residing in the US, we share pay and benefit levels to support pay transparency requirements. For other countries, we are more than happy to share our competitive pay and benefit levels during the interview process.
This is a remote position that requires reliable internet connection and electricity. A monthly stipend is provided to cover expenses associated with working remotely and use of a personal mobile device, if applicable.
NTT Global Data Centers Americas, Inc. offers competitive compensation based on experience, education, and location. Base salary for this position is $171,200 \- $244,500\.
All regular full\-time employees are eligible for an annual bonus; payout is dependent upon individual and company performance.
Employees receive paid time\-off, medical, dental, and vision benefits, life and supplemental insurance, short\-term and long\-term disability, flexible spending account, and 401k retirement plan to create a rich Total Rewards package.
Who we are
As the third largest data center provider, we operate over 150 data centers in more than 20 countries and regions. We understand that every business – large and small – has its own unique needs and goals. We offer local\-to\-global data center expertise, aligned with our connected platform of AI\-ready data centers to create solutions that enable our clients to seamlessly scale their digital businesses, anywhere and anytime.
NTT Global Data Centers is proud to be an Equal Opportunity Employer with a global culture that embraces diversity. We are committed to providing an environment free of unfair discrimination and harassment. We do not discriminate based on age, race, colour, gender, sexual orientation, religion, nationality, disability, pregnancy, marital status, veteran status, or any other protected category. Join our growing global team and accelerate your career with us. Apply today.
Salary Context
This $171K-$244K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At NTT Global Data Centers Americas, Inc., 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 in Demand for This Role
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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($207K) sits 5% below the category median. Disclosed range: $171K to $244K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
NTT Global Data Centers Americas, Inc. AI Hiring
NTT Global Data Centers Americas, Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $244K - $244K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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