Distinguished AI and Data Security Engineer

Verizon•Alpharetta, GA

1d•Hybrid

About The Position

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: Verizon’s Exposure and Vulnerability Management (EVM) Engineering and Reporting team is seeking a Sr. AI and Data Engineer to lead the development of our security data architecture, web infrastructure, and artificial intelligence models. In this role, you will be the hands-on driving force building scalable data pipelines, managing the web infrastructure that hosts our reporting solutions, and developing AI models to predict and prioritize cybersecurity vulnerabilities. While you will build AI and predictive analytics solutions, your primary focus will be engineering the robust data pipelines, databases, and web services that make those intelligent insights possible. You will have the opportunity to influence the blueprint of how diverse asset data is structured, securely hosted, and meaningfully delivered to the security organization. By engineering scalable data models, reliable web infrastructure, and intelligent AI pipelines, you will help ensure our infrastructure is ready for predictive analytics and that our reporting solutions provide proactive, automated insights rather than just historical data.

Requirements

Bachelor's degree or four or more years of work experience.
Eight or more years of relevant experience required, demonstrated through one or a combination of job-related work experience, military experience, or specialized training or education (non-collegiate).
Experience in Python and SQL for complex data manipulation, ETL processes, and algorithmic development.
Advanced expertise in writing, optimizing, and debugging complex queries and schemas for relational (SQL Server, PostgreSQL, Oracle) and non-relational (MongoDB, Elasticsearch) databases, as well as modern Data Warehouses (e.g., Snowflake, BigQuery).
Proven experience deploying, securing, and maintaining web infrastructure, REST APIs, and backend frameworks, as well as working with web servers/proxies.
Hands-on experience developing predictive models and integrating ML libraries into production web environments.
Prior experience engineering within a Vulnerability Management program or Cyber Security organization.

Nice To Haves

Exceptional ability to translate client needs into achievable technical requirements and present infrastructure/data roadmaps to executive leadership.
Advanced experience with cloud-native web hosting, containerization (Docker, Kubernetes), and serverless architecture.
Experience fine-tuning Large Language Models (LLMs) or building Retrieval-Augmented Generation (RAG) frameworks to run on top of your data infrastructure.
Understanding of security frameworks (NIST, SOX, FISMA) and the full IT stack (OS, Network, Cloud, SDLC).

Responsibilities

Design, construct, and maintain highly scalable data pipelines (ETL/ELT) that ingest, transform, and centralize complex security data from numerous Systems of Record (SoR) into the Cyber Data Hub.
Build, maintain, and optimize the backend web infrastructure required to securely serve data dashboards, reporting portals, and AI model endpoints to end-users.
Engineer comprehensive data models (conceptual, logical, physical) and optimize relational and non-relational database architectures for both high-volume transactional processing and BI/analytics workloads.
Develop and deploy machine learning models and intelligent automation to enhance vulnerability risk scoring, utilizing the data foundations and infrastructure you have built.
Create reusable, automated frameworks and CI/CD workflows for data processing, web deployments, and model training to ensure low-touch, sustainable operations.
Implement strict data quality checks, schema validations, and compliance safeguards to ensure the absolute integrity of downstream reporting and AI inputs.
Maintain and monitor our web servers, databases, ML staging, and UAT reporting environments to ensure high availability and reliable, secure deployment of EVM products.
Translate complex security program needs into concrete data and infrastructure tasks, guiding junior engineers and serving as a key technical liaison between security operations and core engineering.