Sr Security Platform Engineer, AI and Automation

Global Enterprise Security (GES) partners with the lines of business across Vanguard to protect crew and client information and assets. Our sub-department, Attack Surface Reduction, provides a cohesive view into Vanguard's technical attack surface, focused on identifying, prioritizing, and managing vulnerabilities and configuration issues across the organization.

DevSecOps, part of Attack Surface Reduction, enhances the security posture of Vanguard's software applications through security automation that helps developers find and fix vulnerabilities earlier in the SDLC.

This role sits on the team responsible for orchestration and enforcement of our security scanning capabilities, including SAST, SCA, container scanning, and API security scanning. We build the platform that runs these scans across Vanguard's pipelines and enforces security policies, and we are extending it with AI capabilities that analyze findings and recommend remediation for developers. You will work closely with the engineers within our team, architects, Security Architecture and Engineering (SAEP), AI, Data and Platform Technology (ADAPT), IAM, Technical Security Advisors and teams across Attack Surface Reduction to shape how security testing is delivered at scale.

Responsibilities:

• Lead the design, development, and implementation of secure, cloud-native platform services on AWS, leveraging technologies such as IAM, ECS, Lambda, Step Functions, RDS, S3, and CloudFormation.
• Integrate application security tools and scanning capabilities into Software Development Life Cycle (SDLC) pipelines, including onboarding new security scanners, normalizing findings, and translating results into enforceable security controls.
• Partner with tech lead and architects on the technical direction for AI scanning capabilities and the AppSec AI Harness, taking agentic vulnerability analysis and remediation from proof of concept to a supported production capability.
• Design integration patterns between AI capabilities and the security orchestration platform, including ingestion of security findings, validation of AI-generated remediation, and measurement of output quality as models and prompts evolve.
• Partner with technical leads and architects to integrate AI capabilities into the broader platform architecture, engineering standards, and delivery roadmap.
• Serve as a technical subject matter expert for AI-enabled application security, establishing practical approaches for applying AI to security challenges across DevSecOps.
• Design and develop APIs, event-driven integrations, automation services, and CI/CD capabilities that enable reliable, scalable security testing and simplify adoption for development teams.
• Improve the reliability and scalability of the orchestration platform and AI capabilities through automation, observability, and self-service capabilities.
• Collaborate with security and engineering teams to embed secure development practices throughout the development lifecycle, including Static Application Security Testing (SAST), Software Composition Analysis (SCA), container security, and API security testing.
• Provide technical leadership and mentorship, promoting high-quality software engineering practices and strengthening the team's capabilities in AI, application security, and platform engineering.
• Partner with stakeholders to establish technical direction, manage technical risks, and deliver business-critical solutions, including determining where AI provides meaningful value within application security.
• Collaborate with DevSecOps leaders to align engineering priorities and deliverables with strategic initiatives as business and technology needs evolve.
• Drive continuous improvement across platform architecture, engineering standards, security practices, and operational excellence.

Required Qualifications

• 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; graduate degree preferred.
• Strong expertise in AWS cloud technologies and cloud-native application and platform architectures.
• Strong software engineering and programming fundamentals, with the ability to work effectively across multiple programming languages. Current technologies include Node.js and TypeScript, with Python 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 degradation after deployment.
• 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.

Preferred Qualifications

• 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 application and product teams across an enterprise.
• 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.

Special Factors

Sponsorship

About Vanguard

At Vanguard, we don't just have a mission—we're on a mission.

To work for the long-term financial wellbeing of our clients. To lead through product and services that transform our clients' lives. To learn and develop our skills as individuals and as a team. From Malvern to Melbourne, our mission drives us forward and inspires us to be our best.

How We Work

Vanguard has implemented a hybrid working model for the majority of our crew members, designed to capture the benefits of enhanced flexibility while enabling in-person learning, collaboration, and connection. We believe our mission-driven and highly collaborative culture is a critical enabler to support long-term client outcomes and enrich the employee experience.