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About This Role
Key Responsibilities
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### Solution Design \& Technical Execution
- Design, build, and deploy technology solutions that drive operational efficiency, automation, and business transformation.
- Translate business requirements into technical architectures, workflows, and scalable implementation plans.
- Lead end\-to\-end execution of technology initiatives, including requirements gathering, solution design, testing, deployment, and adoption.
- Develop integrations across enterprise platforms using APIs, web services, and automation technologies.
- Partner with business and technology stakeholders to identify opportunities for process optimization and digital transformation.
### AI, Automation \& Process Transformation
- Build and deploy AI\-powered solutions, including Microsoft Copilot Studio agents and other generative AI applications.
- Design intelligent workflows using Microsoft Power Platform, Power Automate, and related automation technologies.
- Identify repetitive business processes and implement automation strategies that reduce manual effort and improve operational performance.
- Evaluate emerging AI and automation capabilities and recommend practical use cases that deliver measurable business value.
### Analytics \& Data Engineering
- Develop and maintain executive dashboards and reporting solutions that consolidate data from multiple enterprise systems.
- Perform advanced data analysis to identify trends, opportunities, risks, and operational insights.
- Build scalable reporting and visualization solutions utilizing Power BI and other business intelligence tools.
- Design and optimize data pipelines, ensuring accuracy, accessibility, and governance of business\-critical information.
### Leadership \& Stakeholder Management
- Present technical solutions, implementation progress, and business outcomes to senior leadership and executive stakeholders.
- Influence decision\-making through clear storytelling, data\-driven recommendations, and business case development.
- Facilitate cross\-functional collaboration across operations, technology, analytics, and business teams.
- Serve as a trusted advisor on technology strategy, automation opportunities, and AI adoption.
### Preferred Qualifications / Core Requirements
- Hands\-on experience designing, developing, and deploying AI, automation, and intelligent workflow solutions (Copilot Studio, Generative AI, AI Agents, Power Automate, Agentic AI).
- Strong expertise in data analytics, business intelligence, and dashboard development using Power BI, SQL, and enterprise reporting tools.
- Proven ability to design technical solutions and system integrations, translating business requirements into scalable architectures and automated processes.
- Advanced SQL, data engineering, and data transformation skills, including experience with data pipelines, governance, and reporting architecture.
- Deep knowledge of the Microsoft Power Platform, including Power BI, Power Apps, Power Automate, and Copilot Studio.
- Exceptional executive communication and stakeholder management skills, with the ability to present technical solutions and business value to senior leaders.
- Demonstrated success leading end\-to\-end solution delivery from requirements gathering through implementation, adoption, and measurable business outcomes.
- Experience personally building and deploying technical solutions, not solely managing projects, vendors, or development teams.
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.
Our Lead Tech Program Mgmt earns between $118,800 \- $178,200 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
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Ready to join our team? Apply today!
Our Lead Tech Business Mgmt jobs earn between $118,800\.00 \- $178,200\.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, TexasSalary Range:
$118,800\.00 \- $178,200\.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 $118K-$178K range is below 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 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. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($148K) sits 32% below the category median. Disclosed range: $118K to $178K.
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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