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About This Role
Posted Date
6/04/2026
Description
#### When you join Verizon
You want more out of a career. A place to share your ideas freely — even if they’re daring or different. Where the true you can learn, grow, and thrive. At Verizon, we power and empower how people live, work and play by connecting them to what brings them joy. We do what we love — driving innovation, creativity, and impact in the world. Our V Team is a community of people who anticipate, lead, and believe that listening is where learning begins. In crisis and in celebration, we come together — lifting our communities and building trust in how we show up, everywhere \& always. Want in? Join the \#VTeamLife.
What you’ll be doing...
As the Associate Director of Systems Architecture for Revenue Operations, you will be the primary architect and "AI Copilot" for our GTM engine. You will lead a specialized team of contracted engineers to move beyond basic analytics and build agentic workflows and RAG systems that directly supercharge our sales and marketing teams. This is a high\-impact, hybrid role requiring a "hacker" mindset to rapidly prototype AI\-native tools that eliminate friction in the sales journey.
You will be working to architect and deploy Generative AI solutions that eliminate friction in our Go\-To\-Market (GTM) engine. You will move beyond traditional data analytics and modeling to design and build agentic workflows and RAG (Retrieval\-Augmented Generation) systems that drive a create intuitive, AI\-native employee experience to our GTM representatives that supercharge our sales and marketing teams.
Architectural Design, Strategy \& Mentorship
- Blueprinting: Design large\-scale AI solutions, their architecture and the integration with modern enterprise cloud ecosystems
- Discovery: Exploring and eliminating other GTM friction points by rapid prototyping AI solutions for lead enrichment, personalized outreach, and CRM automation.
- Technical Strategy: Drive the strategy of the AI engineering technical team and manage its execution, keeping up to speed with the latest industry knowledge and methods for AI/ML
- Mentorship: Provide support and guidance to teammates, fostering a culture of technical excellence and sharing advanced AI methodologies
- Responsible AI: Drive the standards and principles that ensure all AI processes are transparent and secure, contributing to the definition of KPIs and auditory requirements
- AI Evangelism \& Enablement: Act as the "AI Copilot" for the revenue and GTM teams. Run internal workshops, drive EnGRAM approvals, create Gemini Gems and agent instruction files, and teach the broader GTM org how to leverage AI tools to 10x their speed and conversion rates.
AI\-Native Engineering \& Execution
- Value\-Driven Deployment: Own the whole lifecycle of the AI\-powered solutions, from build to deployment to performance and cost monitoring
- System Optimization: Constantly review and refine system performance for scalability and cost effectiveness
- Developing and maintaining production\-grade infrastructure for AI orchestration, including prompt management, low\-latency streaming APIs, and model evaluation frameworks.
- Drive Rapid "Vibe Coding" \& Prototyping: Use tools like Claude Code, Codex, Lovable, LangSmith Agent Builder, to go from idea to deployed full\-stack MVP in days, not weeks.
What we’re looking for...
You’ll need to have:
The AWS Stack \& Infrastructure
- Infrastructure as Code (IaC): Deep expertise in AWS CloudFormation/CDK to ensure all virtual services are secure, version\-controlled, and auditable
- AI \& Orchestration: Expertise in AWS Bedrock, Bedrock AgentCore, Strands
- Environment \& Security: Familiarity with AWS IAM, VPC, and networking infrastructure to maintain a "security\-first" architecture
- Data Foundation: Experience with AWS S3, Redshift, and Aurora as the backbone for AI\-powered solutions.
AI\-Native Software Engineering skills
- Python (Programming Language): Advanced proficiency to guide AI agents and ensure the technical foundation is robust
- Agile Methodology: Lead the team through iterative development cycles, driving continuous improvement and continuous integration (CICD) in AI/ML workflows
- Agent\-assisted coding: familiarity with a responsible use of code assistance tool such as Claude Code, AWS Kiro or Github Copilot
- Experience with Lambdas, Step Functions, API gateways and serverless solutions
Management \& Leadership Skills
- A product\-oriented and revenue\-focused mindset: the ability to deeply empathize with a representative's workflow and design AI outputs that are instantly actionable during a live call.
- Evangelist Energy: You are genuinely excited about AI. You follow the latest releases, read the relevant papers, and can explain why "reasoning models" matter to a non\-technical sales or customer success stakeholder.
- Methodology: You must possess an 'Agile\-for\-GTM' approach: prioritizing the rapid feedback loops of a sales cycle over rigid, long\-term development sprints. We need someone who can use Agile ceremonies to manage the inherent uncertainty of AI outputs while maintaining a high\-velocity deployment schedule.
- Tooling: Expert\-level proficiency in Agile project management tools (e.g., Jira, Azure DevOps, or Linear) to track experimentation as well as production tasks as well as standard software development tools (Git, Docker) and CI/CD processes.
Even better if you have one or more of the following:
- Domain expertise in Go\-To\-Market, RevOps, or Sales tech stacks (Salesforce, Amazon Connect, Gong, Salesloft, Marketo, Gainsight).
- Experience building real\-time audio/speech\-to\-text processing pipelines (e.g., Amazon Connect/Kinesis, WebRTC, Deepgram, Whisper) for live transcription and analysis.
- Familiarity with the LangChain platform and ecosystem
- Experience with AI evaluation and observability tools
- Knowledge of "agentic" design patterns
#### Where you’ll be working
In this hybrid role, you'll have a defined work location that includes working from home and a minimum of three days per week in the office, which will be set by your manager. Employees are responsible for maintaining compliance with hybrid work policies.#### Scheduled Weekly Hours
40#### Equal Employment Opportunity
Verizon is an equal opportunity employer. We evaluate qualified applicants without regard to veteran status, disability or other legally protected characteristics.
#### Benefits and Compensation
Our benefits are designed to help you move forward in your career, and in areas of your life outside of Verizon. From health and wellness benefit options including: medical, dental, vision, short and long term disability, basic life insurance, supplemental life insurance, AD\&D insurance, identity theft protection, pet insurance and group home \& auto insurance. We also offer a matched 401(k) savings plan, up to 8 company paid holidays per year and up to 6 personal days per year, paid parental leave, adoption assistance and tuition assistance, plus other incentives, we’ve got you covered with our award\-winning total rewards package. Depending on the role, employees have the opportunity to receive compensation in the form of premium pay such as overtime, shift differential, holiday pay, allowances, etc. Newly hired employees receive up to 15 days of vacation per year, which grows with additional service. For part\-timers, your coverage will vary as you may be eligible for some of these benefits depending on your individual circumstances.
The salary will vary depending on your location and confirmed job\-related skills and experience. This is an incentive based position with the potential to earn more. For part\-time roles, your compensation will be adjusted to reflect your hours.The annual salary range for the location(s) listed on this job requisition based on a full\-time schedule is: $126,000\.00 \- $242,000\.00\.The annual salary range for the Illinois location(s) listed on this job requisition based on a full\-time schedule is: $138,500\.00 \- $242,000\.00\.
Salary
126,000\.00 \- 242,000\.00 Annual
Type
Full\-time
Salary Context
This $126K-$242K range is above the median for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Information Technology Senior Management Forum, 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 $178,940 based on 11,900 positions with disclosed compensation. Director-level AI roles across all categories have a median of $243,000. Disclosed range: $126K to $242K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Information Technology Senior Management Forum AI Hiring
Information Technology Senior Management Forum has 33 open AI roles right now. They're hiring across Data Scientist, Data Engineer, AI Software Engineer, AI/ML Engineer. Positions span McLean, VA, US, Jersey City, NJ, US, Irving, TX, US. Compensation range: $126K - $392K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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