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About This Role
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 a Senior Agentic AI Engineer within our team, you will be at the forefront of Verizon's transformation toward centralized intelligence and enterprise-grade AI agents that drive action. Our team manages a broad portfolio of business processes underpinned by analytics, and you will lead the evolution from reactive reporting to autonomous, intelligent execution. You will architect and deploy multi-agent systems capable of reasoning, planning, and collaborating across complex business domains—closing the loop between AI investment and measurable business outcomes.
Responsibilities include:
- Architecting and deploying scalable multi-agent AI systems—designing agent workflows, communication protocols (A2A), and task delegation hierarchies for fleets of cooperative AI agents.
- Integrating LLMs with enterprise systems via APIs, function calling, and the Model Context Protocol (MCP) to enable agents that can reason, plan, use tools, and act autonomously.
- Building and operating enterprise Agent Factory pipelines—moving from reactive, episodic analytics to autonomous, continuously executing intelligent workflows.
- Designing and implementing retrieval-augmented generation (RAG) systems with ONNX-based embeddings and vector databases to power context-aware, hyper-personalized outputs at scale.
- Managing agent memory and state architecture—implementing short-term, medium-term, and long-term memory using tools like LangGraph for reliable multi-step task execution.
- Owning the full agent and model lifecycle: feature engineering, training, production deployment (Cloud Run, Vertex AI), and continuous monitoring with observability tooling.
- Establishing security guardrails, permission boundaries, short-lived agent identity tokens, and safety constraints for production agentic systems.
- Developing and maintaining propensity models, classification systems, and forecasting solutions that feed downstream agent decision-making.
- Translating complex agentic AI outputs into clear operational strategies and presenting findings to senior leadership and cross-functional business partners.
- Performing advanced AI-augmented analytics and generative AI model validation to ensure accuracy, reliability, and measurable business performance.
What we're looking for…
You are a senior AI engineer who has moved beyond building models into building agents—systems that reason, plan, and act. You have a strong foundation in data science and machine learning, and you've extended that into LLM orchestration, multi-agent architecture, and production agentic systems. You have the strategic vision to evaluate when an autonomous agent is the right solution, and the communication skills to explain complex, probabilistic AI behaviors clearly to executive, security, and business stakeholders.
You'll need to have:
- Bachelor's degree or four or more years of work experience.
- Four or more years of relevant experience required, demonstrated through one or a combination of work and/or military experience, or specialized training.
- Four or more years of relevant work experience, with two or more years focused specifically on Generative AI, LLMs, and autonomous agent systems.
- Four or more years of experience developing and implementing analytical or AI solutions to complex business problems.
- Hands-on proficiency with multi-agent orchestration frameworks: LangChain, LangGraph, Google Agent Development Kit (ADK), LlamaIndex, or AutoGen.
- Experience designing agent-to-agent (A2A) communication protocols, task delegation hierarchies, and the Model Context Protocol (MCP) for enterprise tool integration.
- Experience with agent memory and state management: short-term context, long-term storage, context window optimization, and stateful workflow execution.
- Demonstrated knowledge of retrieval-augmented generation (RAG), ONNX-based embeddings, vector databases (Pinecone, Weaviate, pgvector), and semantic search.
- Demonstrated knowledge of cloud-scale data engineering: BigQuery pipelines and GCP (Cloud Run, Vertex AI, Amazon Bedrock, or Azure equivalent).
- Experience with Python and SQL for statistical modeling, prompt engineering, token management, and large-scale data extraction.
Even better if you have one or more of the following:
- A Master's degree in a quantitative discipline such as Data Science, Computer Science, Statistics, Mathematics, Engineering, or Operations Research.
- Familiarity with ML/LLM monitoring and observability tooling (e.g., OpenTelemetry, Arize Phoenix, Galileo) for tracking model drift, accuracy degradation, and agent output quality over time.
- Experience with generative AI-augmented analytics: AI testing frameworks, model validation pipelines, and LLMOps tooling including observability and token management.
- Experience with AI governance, security, compliance, and financial ROI modeling for AI agent deployments.
- Domain expertise in customer, churn prediction, customer lifetime value (CLV) modeling, or related commercial analytics in Consumer, Telecommunications, Financial Services, or Technology industries.
- A passion for educating and communicating AI findings with integrity to all levels—from reviewing raw outputs with colleagues to presenting AI strategy and business impact to executive stakeholders.
- High curiosity, an investigative mindset, and the flexibility to adapt to new challenges while staying focused on the team's deliverables.
If Verizon and this role sound like a fit for you, we encourage you to apply even if you don’t meet every “even better” qualification listed above.
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: $101,000.00 - $194,000.00.
Salary Context
This $101K-$194K range is below the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Verizon, 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($147K) sits 30% below the category median. Disclosed range: $101K to $194K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Verizon AI Hiring
Verizon has 20 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, Data Scientist, AI Safety. Positions span Basking Ridge, NJ, US, Basking Ridge, NJ, Irving, TX, US. Compensation range: $157K - $263K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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