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About This Role
This position requires Daily Office Presence in the listed location(s). No relocation will be provided.
The Principal Cyber AI Architect will drive the development and optimization of advanced architectures (cloud/hybrid), tools, processes, and workflows to predict, detect, and prevent AI specific threats. This hands\-on technical leadership role combines deep research in AI security with close collaboration across teams to integrate AI capabilities into robust cybersecurity architectures.
This is your chance to shape the future of secure AI/ML applications while working with a team of visionaries. Be part of a transformative journey where your expertise and creativity will drive innovation in a fast\-paced, cutting\-edge field. Empower AT\&T to operate artificial intelligence responsibly and effectively across the business. Safeguard AI systems while upholding the highest standards of security, governance, ethics, and innovation. Mitigate cybersecurity risks associated with AI adoption to unlock its transformative potential.
In the role of Principal, AI Cybersecurity, you will help design, implement, and secure systems from emerging AI cyber threats. You will leverage your advanced expertise to develop innovative solutions, shape organizational objectives, and enable compliance with industry standards. This high\-impact role involves close collaboration with leaders to drive meaningful operational change by securing AT\&T’s AI initiatives.
Key Responsibilities:
- Design, develop, and optimize AI/AISecurity specific architectures, threat models, tools, solutions for threat identification, prediction, and prevention.
- Implement and secure agentic and MCP architectures, neural networks, and AI techniques to enhance threat detection, monitoring, and risk scoring.
- Integrate AI security tools and technologies across AI architectures, collaborating with data scientists, security engineers, and other stakeholders.
- Analyze AI security risk scoring and incident data to refine and improve AI architectures and methodologies.
- Provide technical leadership and mentorship to junior engineers in Agentic and MCP architectures
- Ensure alignment and compliance with industry standards (NIST AI\-RMF, ISO42001, OWASP Top 10 for LLM), and advanced security architectures (Agentic, MCP).
- Stay abreast of emerging trends and advancements in AI and cybersecurity.
Qualifications:
- Bachelor's or Master's degree in computer science, Engineering, or a closely related discipline is preferred
- 7\+ years of experience in AI and cloud\-focused architectures and cybersecurity in an enterprise environment.
- Expertise in Python, R, Java, or similar programming languages.
- Deep hands\-on understanding of agentic frameworks (LangGraph, LangChain, Crew, OpenClaw etc.,), and AI orchestration.
- Hands\-on experience with AI security technologies (intrusion detection, anomaly detection, threat intelligence).
- 5\+ years’ experience in Azure or AWS cloud native services, architectures, and tools.
- Expertise in enterprise architecture (including Cloud\-native and AI architecture patterns).
- Advanced knowledge of security and governance frameworks (NIST AI\-RMF, ISO42001, OWASP Top 10 for LLM).
- Strong communication and collaboration skills.
Preferred Qualifications:
- Experience with agentic and model context protocols (MCP) architectures.
- Demonstrated ability to lead cross\-functional technical teams.
- Track record of published research or thought leadership in AI security.
AT\&T will not hire candidates for this position who require sponsorship now or in the future
Our Principal Cybersecurity jobs earn between $155,400\.00 \- $261,100\.00 USD Annual. Not to mention all the other amazing rewards that working at AT\&T offers. Individual starting salary within this range may depend on geography, experience, expertise, and education/training.
Joining our team comes with amazing perks and benefits:
- Medical/Dental/Vision coverage
- 401(k) plan
- Tuition reimbursement program
- Paid Time Off and Holidays (based on date of hire, at least 23 days of vacation each year and 9 company\-designated holidays)
- Paid Parental Leave
- Paid Caregiver Leave
- Additional sick leave beyond what state and local law require may be available but is unprotected
- Adoption Reimbursement
- Disability Benefits (short term and long term)
- Life and Accidental Death Insurance
- Supplemental benefit programs: critical illness/accident hospital indemnity/group legal
- Employee Assistance Programs (EAP)
- Extensive employee wellness programs
- Employee discounts up to 50% off on eligible AT\&T mobility plans and accessories, AT\&T internet (and fiber where available) and AT\&T phone
Weekly Hours:
40Time Type:
RegularLocation:
Dallas, Texas, Middletown, New Jersey, Plano, Texas, USA:NC:Charlotte / Ibm Dr \- Adm:8505 Ibm DrSalary Range:
$155,400\.00 \- $261,100\.00
AT\&T and its subsidiaries are committed to equal employment opportunity. All hiring, promotion, and other employment decisions remain merit\-based and free from discrimination on the basis of race, color, religion, religious creed, national origin, ancestry, age, sex, sexual orientation, gender, gender identity, gender expression, physical disability, mental disability, pregnancy, medical condition, genetic information, marital status, citizenship status, military status, veteran status, or any other characteristic protected by federal, state, or local laws. In addition, AT\&T will provide reasonable accommodations to qualified individuals with disabilities. AT\&T is a fair chance employer and does not initiate a background check until an offer is made. Click here to learn more or request an application accommodation here.
Salary Context
This $155K-$261K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At AT&T, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. Disclosed range: $155K to $261K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
AT&T AI Hiring
AT&T has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Middletown, NJ, US, Atlanta, GA, US. Compensation range: $237K - $427K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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