Senior Agentic AI Engineer
for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). We need to include sections only when posting has real specific content. We have content: responsibilities list, requirements list, qualifications list (maybe overlapping), skills list, benefits and compensation, schedule (hybrid role, 40 hours). Also pay: salary range given. So we need sections: About the role (maybe the intro paragraph), Responsibilities, Requirements (maybe combine qualifications?), Skills, Benefits, Pay, Schedule.
We must not repeat job title as heading. No Job ID etc. Remove boilerplate: EEO, equal opportunity, scam warning, application instructions, cookie/navigation/legal. Also remove duplicated lines.
We need to preserve all substantive info. So we need to extract the relevant paragraphs.
Let's parse the posting:
Intro: "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."
That's about company culture. Could be "About the role" or maybe "About Verizon". We'll use About the role
and put that paragraph(s) in
. Next: "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." That could be part of responsibilities, but there is a separate "Responsibilities Include" list. We'll treat the paragraph as part of responsibilities intro, but we can include it in responsibilities section as prose before list. Then responsibilities list bullet points (starting with "Architecting and deploying scalable multi-agent AI systems—designing agent workflows, communication protocols (A2A), and task delegation hierarchies for fleets of cooperative AI agents." etc). We'll put each as
About the 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.
Responsibilities
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.
- 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.
Requirements
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.
- 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.
Skills
- 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.
Benefits
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.
Pay
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.
Schedule
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