Senior Agentic AI Engineer

We need to output HTML fragment with sections: About the role? The posting includes intro paragraphs about Verizon, then "What You'll Be Doing…" which is responsibilities. Then "What We're Looking For…" which is qualifications/requirements. Then "Where you’ll be working" (Schedule/hybrid). Then "Scheduled Weekly Hours 40". Then "Benefits And Compensation" includes benefits and salary range. We must not repeat job title as heading. Use

for section headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We need to decide which sections to include based on content. We have content: Intro about Verizon could be "About the role" maybe? Actually "About the role" could be the introductory description of the role and team. We'll put that in

About the role

with

containing the intro paragraphs. Responsibilities: "What You'll Be Doing…" list of duties. Use

• . Requirements/Qualifications/Skills: The posting has "What We're Looking For…" with bullet-like paragraphs. Could split into Requirements (basic) and Qualifications (preferred) or Skills. We have "You'll Need To Have" list (requirements) and "Even better if you have one or more of the following:" (preferred). We could combine into Requirements section with both required and preferred? But better to separate: Requirements (mandatory) and Qualifications (preferred) or Skills. The instruction: use headings for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). We can include Requirements, Qualifications, Skills as separate if content exists. Let's parse: Requirements (mandatory): - Bachelor's degree or four or more years of work experience. - Four or more years of relevant experience required... - 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. Qualifications (preferred/"Even better if you have"): - Master's degree in a quantitative discipline... - Familiarity with ML/LLM monitoring and observability tooling... - Experience with generative AI-augmented analytics... - Experience with AI governance, security, compliance, and financial ROI modeling... - Domain expertise in customer, churn prediction, CLV modeling... - Passion for educating and communicating AI findings... - High curiosity, investigative mindset, flexibility... Skills could be extracted but we already have many skill items in requirements. Could create Skills section summarizing technical skills: multi-agent orchestration, A2A, MCP, agent memory, RAG, ONNX embeddings, vector DBs, cloud data engineering, Python, SQL, etc. But we might avoid duplication. The instruction: prefer a few substantive sections over many thin ones. So maybe combine Requirements and Qualifications into one "Requirements" section, and maybe a "Skills" section for technical skills. But we have many skill items already in requirements; we could keep them there. We also have Schedule/hybrid: "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". So we can have Schedule section with that content. Benefits: "Benefits And Compensation" includes benefits description and salary range. We need Benefits and Pay sections separately if content exists. Benefits: list of health, wellness, 401(k), holidays, personal days, parental leave, etc. Pay: salary range $101,000 - $194,000, plus note about incentive based position, etc. We'll create Benefits section with
  • for each benefit item. Pay section with

    containing salary range and other compensation details. We must not include boilerplate like EEO, etc. Remove those. We must not include application instructions. We must not repeat job title. We must not include headings with filler text. We must output only HTML fragment. Let's craft. Structure:

    About the role

    Intro paragraphs about Verizon, V Team, etc.

    Responsibilities

    • ... each responsibility line.

    Requirements

    • ... each requirement line (mandatory).

    Qualifications

    • ... each preferred qualification.

    Skills

    Maybe not needed if we already covered. But we could combine Requirements and Qualifications into one "Requirements" and then have "Skills" for technical skills. However we already have many technical skills in requirements; could still have Skills summarizing them. But to avoid thin sections, maybe we just have Requirements (including both mandatory and preferred) and Skills (technical). Let's decide: We'll have Requirements (mandatory) and Qualifications (preferred). Then Skills could be derived from the requirements but might be redundant. Could skip Skills and just have Requirements and Qualifications. The instruction says we can use those headings if content exists. We have content for Requirements and Qualifications. So we will include both.

    Schedule

    Hybrid work details and weekly hours.

    Benefits

    • Medical, dental, vision
    • Short and long term disability
    • Basic life insurance, supplemental life insurance, AD&D insurance
    • Identity theft protection
    • Pet insurance
    • Group home & auto insurance
    • Matched 401(k) savings plan
    • Up to 8 company paid holidays per year
    • Up to 6 personal days per year
    • Paid parental leave, adoption assistance
    • Tuition assistance
    • Other incentives (award-winning total rewards package)

    Pay

    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. This is an incentive based position with the potential to earn more. For part‑time roles, compensation will be adjusted to reflect hours.

    We need to ensure we don't include filler like "commensurate with experience". Not present. Check for any duplicated lines: we must avoid repeating same content. Now produce HTML. Let's write the About the role paragraph: combine the first few paragraphs: "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." Make sure proper punctuation. Now Responsibilities list: each bullet from "What You'll Be Doing…" after that heading. Let's extract each sentence that starts with a verb. List: - 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. Make each as
  • . Now Requirements (mandatory) list from "You'll Need To Have". Let's extract each bullet: - 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. Now Qualifications (preferred) list from "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