Sr Security Platform Engineer, AI and Automation

Vanguard•Malvern, PA

1d•Hybrid

About The Position

Global Enterprise Security (GES) partners with Vanguard’s lines of business to protect crew and client information and assets. Attack Surface Reduction provides a cohesive view of Vanguard’s technical attack surface by identifying, prioritizing, and managing vulnerabilities and configuration issues. Within Attack Surface Reduction, DevSecOps improves application security through automation that helps developers find and fix vulnerabilities earlier in the software development lifecycle (SDLC). This role is on the team responsible for orchestrating and enforcing security scanning capabilities, including SAST, SCA, container scanning, and API security scanning. The team builds the platform that runs scans across Vanguard’s pipelines and enforces security policies, and is extending the platform with AI capabilities that analyze findings and recommend remediation. The role works with engineers, architects, Security Architecture and Engineering (SAEP), AI, Data and Platform Technology (ADAPT), IAM, Technical Security Advisors, and teams across Attack Surface Reduction. The engineer leads development of secure, cloud-native platform services on AWS and advances AI-enabled application security capabilities from proof of concept to production. The role combines platform engineering, application security, AI integration, technical leadership, operational excellence, and collaboration across organizational boundaries. Vanguard describes its work model as hybrid for the majority of crew members, supporting flexibility alongside in-person learning, collaboration, and connection.

Requirements

12+ years of experience in software engineering, application development, or platform engineering, including 8+ years of hands-on software development experience.
Undergraduate degree or an equivalent combination of relevant education, training, and experience.
Strong expertise in AWS cloud technologies and cloud-native application and platform architectures.
Strong software engineering and programming fundamentals, with the ability to work across multiple programming languages; current technologies include Node.js and TypeScript, and Python is particularly valuable for AI and large language model development.
Hands-on experience building agentic AI solutions, including multi-step, tool-using agents integrated into production systems.
Experience integrating large language models (LLMs) into production applications, including prompt design, tool/function calling, context management, cost optimization, and latency management.
Practical experience evaluating the quality and reliability of AI-generated output, including establishing release criteria and identifying post-deployment degradation.
Experience designing and implementing APIs, distributed systems, automation solutions, and CI/CD pipelines.
Experience operating production platform services, including applying observability, reliability, and operational engineering practices.
Demonstrated ability to lead complex technical initiatives, collaborate and influence across organizational boundaries, and mentor engineers.
Ability and willingness to learn and apply new technologies as platforms, AI capabilities, and security requirements evolve.

Nice To Haves

Graduate degree.
Experience with Amazon Bedrock, including model selection, guardrails, and evaluating trade-offs among hosted models.
Experience in application security, DevSecOps, or secure software development, including commercial security scanning platforms such as Snyk, Wiz, or JFrog Xray.
Experience building automated code remediation or developer-facing security tooling, particularly solutions where accuracy and false-positive management are critical to developer adoption.
Experience partnering with enterprise AI and data platform teams to use shared AI services while aligning with organizational standards for model access and data handling.
Experience in platform engineering, DevOps, or CI/CD engineering, including building services and pipelines used by enterprise application and product teams.
Experience designing and delivering enterprise-scale automation solutions adopted by multiple teams.
AWS certifications such as AWS Certified Solutions Architect - Associate or Professional, AWS Certified DevOps Engineer - Professional, or AWS Certified Machine Learning Engineer - Associate.
Demonstrated applied AI engineering experience through production AI systems, open-source contributions, or published work; practical demonstrated experience is valued over certification alone.

Responsibilities

Lead the design, development, and implementation of secure, cloud-native platform services on AWS.
Integrate application security tools and scanning capabilities into SDLC pipelines, including onboarding scanners, normalizing findings, and translating results into enforceable security controls.
Partner with technical leads and architects on the direction of AI scanning capabilities and the AppSec AI Harness, taking agentic vulnerability analysis and remediation from proof of concept to supported production capability.
Design integrations between AI capabilities and the security orchestration platform, including finding ingestion, validation of AI-generated remediation, and measurement of output quality as models and prompts evolve.
Integrate AI capabilities into broader platform architecture, engineering standards, and delivery roadmaps.
Serve as a technical subject matter expert for AI-enabled application security and establish practical approaches for applying AI to DevSecOps challenges.
Design and develop APIs, event-driven integrations, automation services, and CI/CD capabilities for reliable, scalable security testing and easier developer adoption.
Improve the reliability and scalability of orchestration platform and AI capabilities through automation, observability, and self-service.
Collaborate with security and engineering teams to embed secure development practices throughout the development lifecycle, including SAST, SCA, container security, and API security testing.
Provide technical leadership and mentorship, promoting high-quality software engineering and strengthening team capabilities in AI, application security, and platform engineering.
Partner with stakeholders to establish technical direction, manage technical risks, and deliver business-critical solutions, including assessing where AI provides meaningful value in application security.
Align engineering priorities and deliverables with strategic initiatives in collaboration with DevSecOps leaders as business and technology needs evolve.
Drive continuous improvement in platform architecture, engineering standards, security practices, and operational excellence.