Principal AI Security Developer

Overview

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.

As a Principal AI Security Developer, you will be the driving force behind the next generation of Verizon's AI-driven defensive capabilities. We are looking for a deep-tech security practitioner who specializes in building and securing the AI systems that protect our infrastructure. You will partner directly with Cyber leadership to ideate and deliver high-concurrency, AI-powered applications that automate compliance flows, security operations processes, and other critical areas of our Cybersecurity organization.

You aren't just writing code; you are engineering the future of Cyber AI Assurance, ensuring our AI workflows are resilient against prompt injection, data poisoning, and model inversion. If you are a security expert who views AI as your primary tool for scale and defense, this is your role.

• Business Translation & Proactive Ideation: Consulting directly with Cyber business leaders to deeply understand operational challenges and proactively ideating, designing, and translating high-level business needs into defined, technical requirements and actionable, high-value AI-powered applications.

• Cyber AI Assurance & Security: Ensuring developed AI/ML systems and agentic workflows adhere to Verizon Cybersecurity standards, including secure coding practices, vulnerability management, and the implementation of AI-specific security controls (e.g., addressing prompt injection, data poisoning, and model inversion risks).

• Full Stack Tooling & Visualization: Building high-concurrency APIs using FastAPI and developing front end solutions to support ML-based anomaly detections.

• Automation with GenAI: Designing and deploying AI agents using Lang Graph or CrewAI to automate critical Verizon Cybersecurity functions, including incident triage, reputation lookups, evidence evaluation, and report generation.

• Engineering for Data Science: Architecting and maintaining robust data pipelines to support data scientists working on anomaly detection and threat hunting models.

• Big Data Processing: Managing large-scale log ingestion and processing capabilities within GCP (utilizing Dataflow and Dataproc) and AWS containerized environments.

• Infrastructure & Deployment: Deploying applications to containerized environments (Docker/Kubernetes/GKE), managing CI/CD pipelines to ensure reliability, and supporting model deployment evaluation via Vertex
  
  AI and MLOps practices.

• Observability & Reliability: Implementing deep instrumentation using Open Telemetry (OTel) for instrumenting Python applications and manual instrumentation of complex workflows (Spark, AI Agents), while creating Grafana dashboards to visualize metrics, distributed traces, and logs.

'You'll need to have:'

• Bachelor's degree or four or more years of work experience.

• Six or more years of relevant work experience required, demonstrated through one or a combination of job-related work experience, military experience, or specialized training or education (non-collegiate).

• Background in Cybersecurity or Anomaly Detection.

• Experience in Python and/or JavaScript; proven experience working within Agile methodologies.

• Experience building AI systems and/or multi-agent workflows and/or RAG implementations using frameworks like Lang Chain, Lang Graph, or CrewAI, integrated with Vector Databases (e.g., pgvector) within the Vertex
  
  AI environment or a similar technology.

• Backend experience in FastAPI (asynchronous REST APIs) and/or Flask (microservices) combined with experience managing middleware gateways such as Kong, Apigee, or Api Six or similar technology.

• Experience with Cloud Service providers (GCP, AWS, etc.) deploying and managing resources therein to include containers, compute instances, logging, etc.

• Data Engineering & Big Data:
  Knowledge of Python data science libraries (Pandas, Num Py, Scikit-learn).

• LLM Production & Security:
  Demonstrated success integrating LLMs into production workflows, including MCP implementations, security best practices, and OAuth/OIDC authentication standards.

• Strong background in developing automated testing suites and managing end-to-end deployment pipelines.

• Experience interacting with SIEM APIs (Splunk, Chronicle, Elastic).

• Frontend proven ability with Plotly Dash for complex data visualisation.

• Experience with GKE Looker.

• Proficiency in Apache PySpark for writing,…