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About This Role
This position requires office presence of a minimum of 5 days per week and is only located in the location(s) posted. No relocation is offered.
AT\&T will not hire any applicants for this position who require employer sponsorship now or in the future.
Join AT\&T and help shape the future of communications and technology that connect the world. We value innovators who seek to explore the unknown and challenge the status quo. Bring your bold ideas and fearless spirit to redefine connectivity and transform how people share stories and experiences. At AT\&T, you won’t just imagine the future—you’ll build it.
The Artificial Intelligent (AI) Accelerator Team is a strategic AI product, solutions, and data science organization focused on rapidly identifying business opportunities, developing AI\-powered capabilities, and delivering measurable business impact across organization. The team partners across the enterprise to accelerate innovation, integrate AI into core business processes, and drive value realization at scale.
We are building a high\-performing team of leaders, architects, AI experts, product managers, data scientists, and delivery professionals who can transform ideas into deployed solutions, accelerate adoption, and deliver sustainable business outcomes quickly and effectively.
What you’ll do:
- Design, build, test, deploy, and maintain AI agents, copilots, and intelligent applications.
- Develop Generative AI and Large Language Model (LLM) solutions, including prompt engineering and Retrieval Augmented Generation (RAG).
- Build APIs, integrations, automations, and microservices that enable AI solutions to operate within business processes.
- Collect, prepare, and analyze data to support AI and machine learning models.
- Monitor AI performance, troubleshoot issues, optimize outcomes, and continuously improve solutions after deployment.
- Ensure AI solutions are secure, compliant, governed, and operationally reliable.
- Partner with business, operations, product, and technology teams to translate business challenges into AI\-enabled solutions.
What you’ll bring:
As a Lead Data/AI Engineering, within the AI Accelerator Team, this position will design, build, test, and deploy AI agents, copilots, and intelligent applications, spanning generative AI, LLMs, prompt engineering, RAG, APIs, and integrations, to move from business problems to production. Partners with stakeholders deliver scalable, secure, enterprise\-grade solutions that streamline processes and drive measurable business, operational, and financial results. Working to create fast solutioning for the business.
This is a fast\-paced opportunity to help shape the future of AI within AT\&T Field Operations. We're looking for collaborative leaders who thrive in dynamic environments, embrace change, and are passionate about building culture, developing teams, and delivering meaningful business impact.
What you’ll need:
- Required Experience:
+ 5\+ years of related experience in area of professional responsibility
+ 5\+ years in a technology architect and/or developer role
+ 5\+ years' experience managing direct reports.
+ Certification is required in some areas.
- Required Qualifications:
+ Understanding a business problem to determine if AI can solve it.
+ Builds the agent/model/application
+ Connects it to enterprise systems and data
+ Tests and deploys it to production
- Desired Education:
+ Bachelor’s degree (BS/BA) in Computer Science is preferred
Supervisor: No
Our Lead Data/AI Engineering jobs earn between $141,300\.00 \- $237,400\.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:
Atlanta, Georgia, Dallas, TexasSalary Range:
$141,300\.00 \- $237,400\.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 $141K-$237K 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($189K) sits 12% below the category median. Disclosed range: $141K to $237K.
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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