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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.
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.
Position Overview
We are seeking a Director \- Technology to lead and scale delivery teams building enterprise agentic AI solutions. This leader will own end\-to\-end execution for AI\-native products and platforms, directly managing engineers across multiple disciplines, some to include: LLM/Prompt\-Context Engineering, Backend/Agent Engineering, and Integration Engineering. The ideal candidate combines hands\-on technical depth in AI agent architectures, LLMs, and fullstack Python systems with proven experience leading high\-performing engineering teams that deliver agentic solutions at scale.
This role is responsible for translating strategic AI initiatives into shipped products that drive measurable business value.
What You'll Do:
Delivery Leadership and Team Management
- Lead, grow, and manage a team of engineers delivering AI and Agentic solutions and products.
- Own end\-to\-end delivery execution for agentic AI initiatives, from concept through production, ensuring quality, velocity, and operational readiness.
- Set technical direction, engineering standards, and delivery cadences across all workstreams.
- Build and scale durable in\-house engineering teams, reducing reliance on contractor resources and improving knowledge retention.
- Drive hiring, performance management, career development, and mentorship for direct reports.
AI\-Native Technical Leadership
- Provide architectural oversight for LLM integration, prompt/context engineering, and multi\-agent orchestration using frameworks such as LangGraph.
- Guide teams on context management strategies including session memory, retrieval\-augmented generation (RAG), vector search, and user personalization.
- Ensure scalable, production\-grade deployment of AI agents through robust backend services, APIs, and CI/CD pipelines.
- Drive evaluation, testing, and continuous optimization of prompt effectiveness, agent workflows, and system reliability.
Platform and DevOps Accountability
- Drive DevOps strategy, CI/CD governance, and platform security across delivery workstreams.
- Establish platform security hardening, vulnerability management, and compliance frameworks for AI\-native services.
- Ensure architectural consistency, integration standards, and operational excellence across all agentic solution delivery teams.
- Own incident response, RCA, and continuous improvement for reliability, performance, and delivery velocity.
Cross\-Functional Execution
- Partner with Product, Architecture, and Business stakeholders to translate requirements into delivered outcomes aligned to strategic priorities.
- Drive cross\-functional alignment on technical direction, trade\-offs, roadmaps, and delivery status.
- Coordinate with data science, ML engineering, and front\-end teams to deliver end\-to\-end AI\-powered applications.
What You'll Need:
Leadership Experience
- 8\+ years of progressive technology leadership, with 3\+ years directly managing engineering teams delivering AI/ML or automation solutions.
- Proven track record of leading AI\-native delivery teams from concept through production at enterprise scale.
- Experience managing resources across multiple engineering disciplines (backend, fullstack, AI/ML, DevOps).
- Demonstrated ability to recruit, develop, and retain top engineering talent.
Technical Depth (Hands\-On Experience Required)
- Deep experience with fullstack Python development (FastAPI, Flask, Django; SQL/NoSQL databases).
- Demonstrated expertise in prompt engineering and context engineering for LLMs (OpenAI, Anthropic, open\-source models).
- Hands\-on experience architecting and deploying AI agents and multi\-agent systems in production environments.
- Proficiency with agent orchestration frameworks such as LangGraph.
- Strong understanding of RAG architectures, vector databases, knowledge retrieval strategies, and session/context management.
- Experience with cloud infrastructure (AWS, GCP, Azure), containerization (Docker), and CI/CD pipelines.
- Knowledge of RESTful API design, distributed systems, and scalable backend architectures.
Delivery and Operational Excellence
- Experience establishing engineering standards, DevOps practices, and release governance for AI\-native platforms.
- Track record of improving delivery predictability, reducing rework, and maintaining platform stability.
- Experience with incident management, observability, and production support for AI/ML systems.
Our Director\-Technology jobs earn between $188,100\.00 \- $316,000\.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 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
- Long Term Grants and Deferred Compensation
- Paid Time Off and Holidays (based on date of hire, at least 28 days of vacation each year and 9 company\-designated holidays
Weekly Hours:
40Time Type:
RegularLocation:
Atlanta, Georgia, Bothell, Washington, Middletown, New Jersey, USA:TX:Dallas / Two AT\&T Plaza (211 S Akard St) \- Dat:211 S Akard StSalary Range:
$188,100\.00 \- $316,000\.00
It is the policy of AT\&T to provide equal employment opportunity (EEO) to all persons regardless of age, color, national origin, citizenship status, physical or mental disability, race, religion, creed, gender, sex, sexual orientation, gender identity and/or expression, genetic information, marital status, status with regard to public assistance, veteran status, or any other characteristic protected by federal, state or local law. In addition, AT\&T will provide reasonable accommodations for qualified individuals with disabilities. AT\&T is a fair chance employer and does not initiate a background check until an offer is made.
Salary Context
This $188K-$316K range is above the 75th percentile 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 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($252K) sits 15% above the category median. Disclosed range: $188K to $316K.
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.
AT&T AI Hiring
AT&T has 4 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Middletown, NJ, US, Atlanta, GA, US, San Ramon, CA, US. Compensation range: $178K - $316K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 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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