Interested in this AI/ML Engineer role at Lumen?
Apply Now →About This Role
Lumen is the trusted network for the AI‑powered world, connecting people, data, and applications through our expansive fiber network and connected ecosystem. We enable secure, high‑performance connectivity across cloud, edge, and AI workloads for enterprises, governments, and communities.
At Lumen, you’ll work on infrastructure customers rely on today and build for what’s next, where performance, security, and resilience matter.
This is a high accountability environment where bold ideas drive real innovation for our customers, partners, and industry. The work is challenging, expectations are clear, and trust is built into how we operate. If you’re ready to take ownership, deliver meaningful impact, and help shape the future of AI‑ready connectivity, join us today.
The Role
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The Senior Director, AI Enablement \& Transformation is a senior leadership role within the Technology Services organization responsible for defining and driving the strategic transformation roadmap across IT Infrastructure, Database Operations, Platform Engineering, and Cloud Engineering. This role serves as a key leader in modernizing Technology Services by establishing long\-term strategy, guiding architectural direction, aligning transformation initiatives to business objectives, and ensuring successful execution across multiple technology domains.
The Senior Director partners closely with technology leaders, Principal Architects, program managers, and enterprise stakeholders to translate strategic objectives into measurable outcomes that improve operational efficiency, reliability, scalability, service quality, and organizational effectiveness. As part of advancing Technology Services capabilities, the role evaluates and responsibly leverages emerging technologies, including Artificial Intelligence (AI), Generative AI, automation, and data\-driven insights, to accelerate transformation and drive business value.
Location
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This is a remote opportunity open to candidates located in the U.S.
The Main Responsibilities
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- Define and drive the Technology Services transformation strategy across Infrastructure, Database Operations, Platform Engineering, and Cloud Engineering, aligning priorities with organizational and enterprise objectives.
- Develop and maintain multi\-year transformation roadmaps with clear milestones, success metrics, business outcomes, and value realization targets.
- Partner with technology leaders, Principal Architects, and program managers to identify modernization opportunities and define target\-state architectures, technology patterns, operating models, and transformation priorities.
- Translate strategic objectives into executable initiatives and team\-level workstreams, ensuring roadmap goals are incorporated into delivery plans and technology backlogs.
- Establish governance processes, executive reporting, and review cadences to monitor progress, manage dependencies, address risks, and ensure accountability for transformation outcomes.
- Serve as a trusted advisor to senior technology leaders on modernization strategies, technology investments, operating model evolution, and organizational change initiatives.
- Collaborate with Enterprise Architecture, AI Platform, AI Governance, Security, Privacy, and other shared\-services teams to ensure alignment with enterprise standards, architectural principles, governance requirements, and security expectations.
- Promote the responsible use of AI, Generative AI, automation, analytics, and other emerging technologies to improve operational efficiency, service reliability, workforce productivity, and decision\-making.
- Evaluate industry trends and emerging technologies to identify opportunities that accelerate innovation and enhance Technology Services capabilities.
- Build alignment and influence across stakeholder groups without direct authority while fostering a culture of accountability, collaboration, continuous improvement, and operational excellence.
What We Look For in a Candidate
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- Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field, or equivalent practical experience.
- 12\+ years of experience in technology leadership roles within enterprise IT, infrastructure, cloud, platform, or operations environments.
- 5\+ years of experience leading large\-scale technology strategy, architecture, or transformation initiatives.
- Demonstrated ability to define strategy and translate it into roadmaps, milestones, and measurable outcomes, with executive\-level reporting.
- Proven experience influencing senior stakeholders and driving alignment across multiple technology organizations without direct authority.
- Hands\-on experience prototyping, piloting, or operationalizing AI / Generative AI solutions, with working knowledge of model lifecycle considerations (evaluation, monitoring, and responsible use).
- Strong understanding of enterprise architecture principles and experience partnering with senior architects.
Compensation
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This information reflects the anticipated base salary range for this position based on current national data. Minimums and maximums may vary based on location. Individual pay is based on skills, experience and other relevant factors.
Location Based Pay Ranges
$174,876 \- $233,168 in these states: AL AR AZ FL GA IA ID IN KS KY LA ME MO MS MT ND NE NM OH OK PA SC SD TN UT VT WI WV WY
$183,621 \- $244,827 in these states: CO HI MI MN NC NH NV OR RI
$192,364 \- $256,486 in these states: AK CA CT DC DE IL MA MD NJ NY TX VA WA
Lumen offers a comprehensive package featuring a broad range of Health, Life, Voluntary Lifestyle benefits and other perks that enhance your physical, mental, emotional and financial wellbeing. We're able to answer any additional questions you may have about our bonus structure (short\-term incentives, long\-term incentives and/or sales compensation) as you move through the selection process.
Learn more about Lumen's:
Benefits
Bonus Structure
Requisition \#: 342812
Life at Lumen
Life at Lumen is human and connected, even in a fast moving, AI‑focused organization. We set clear expectations and trust people to meet them. With real support and shared accountability, teams collaborate better, move faster, and deliver meaningful outcomes.
Our Lumen 8 behaviors guide how we interact, make decisions, and work together, shaping a culture built to perform and win.
To learn more about Life at Lumen and how we live the Lumen 8, please visit:
https://jobs.lumen.com/global/en/life\-at\-lumen
Background Screening
If you are selected for a position, there will be a background screen, which may include checks for criminal records and/or motor vehicle reports and/or drug screening, depending on the position requirements. For more information on these checks, please refer to the Post Offer section of our FAQ page. Job\-related concerns identified during the background screening may disqualify you from the new position or your current role. Background results will be evaluated on a case\-by\-case basis.
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
Equal Employment Opportunities
We are committed to providing equal employment opportunities to all persons regardless of race, color, ancestry, citizenship, national origin, religion, veteran status, disability, genetic characteristic or information, age, gender, sexual orientation, gender identity, gender expression, marital status, family status, pregnancy, or other legally protected status (collectively, “protected statuses”). We do not tolerate unlawful discrimination in any employment decisions, including recruiting, hiring, compensation, promotion, benefits, discipline, termination, job assignments or training.
Privacy Notice
Lumen is committed to protecting the privacy and security of personal information collected during the recruitment and hiring process. Our Applicant Privacy Notice explains how we collect, use, disclose, and protect applicant information, as well as how individuals may request access to or deletion of their personal data.
To review Lumen’s Global Employment Applicant and Talent Community Privacy Notice, please visit:
https://jobs.lumen.com/global/en/privacy\-notice
Disclaimer
The job responsibilities described above indicate the general nature and level of work performed by employees within this classification. It is not intended to include a comprehensive inventory of all duties and responsibilities for this job. Job duties and responsibilities are subject to change based on evolving business needs and conditions.
In any materials you submit, you may redact or remove age\-identifying information such as age, date of birth, or dates of school attendance or graduation. You will not be penalized for redacting or removing this information.
Please be advised that Lumen does not require any form of payment from job applicants during the recruitment process. All legitimate job openings will be posted on our official website or communicated through official company email addresses. If you encounter any job offers that request payment in exchange for employment at Lumen, they are not for employment with us, but may relate to another company with a similar name.
Salary Context
This $174K-$256K 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 Lumen, 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. Director-level AI roles across all categories have a median of $272,150. Disclosed range: $174K to $256K.
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.
Lumen AI Hiring
Lumen has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in Remote, US. Compensation range: $124K - $256K.
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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