Lead Application Security Engineer, IT Security

Raymond James•Saint Petersburg, FL

5d•Hybrid

About The Position

The financial services industry is continuously targeted by sophisticated cyber adversaries ranging from criminal organizations to nation-state actors. Raymond James relies on the Cyber Threat Center (CTC) to identify, assess, and reduce technology risk across the enterprise. The Lead Application Security Engineer will be a hands-on technical leader responsible for integrating security into the software development lifecycle, assessing application and API risk, and enabling development teams to deliver resilient software at scale. This role combines application security engineering, software security assessment, vulnerability analysis, secure software development practices, and cybersecurity architecture. The engineer will build and automate security controls across CI/CD pipelines; perform risk-based testing and threat modeling; and responsibly apply AI-assisted techniques to accelerate vulnerability discovery, triage, validation, and remediation.

Requirements

Demonstrated expertise identifying, validating, explaining, and remediating application and API vulnerabilities, including vulnerability classes represented in the OWASP Top 10 and OWASP API Security Top 10.
Advanced understanding of authentication, authorization, session management, cryptography, input handling, deserialization, server-side request forgery, business-logic abuse, and modern client/server attack surfaces.
Hands-on experience with SAST, DAST, IAST, SCA, API testing, secrets detection, container scanning, infrastructure-as-code scanning, and penetration-testing tools; ability to tune controls and validate tool output rather than rely solely on scanner severity.
Strong automation and software engineering capability in Python and at least one of PowerShell, JavaScript/TypeScript, Go, Java, C#, or shell; experience consuming REST/GraphQL APIs, processing structured data, writing tests, and maintaining production-quality code.
Experience integrating security tools with CI/CD and engineering platforms such as GitHub, GitLab, Azure DevOps, Jenkins, Jira, or comparable technologies.
Demonstrated experience applying AI-assisted or machine-learning-enabled security tooling to source-code review, vulnerability triage, exploit-path analysis, test generation, remediation support, or finding correlation.
Ability to critically evaluate AI output, recognize hallucinations and insecure recommendations, protect sensitive source code and data, design human-in-the-loop validation, and establish measurable quality and governance controls.
Knowledge of secure AI-assisted development risks, including prompt injection, insecure output handling, excessive agency, sensitive information disclosure, model or dependency supply-chain concerns, and misuse of generated code.
Experience securing cloud-native applications on Microsoft Azure, Amazon Web Services, and/or Google Cloud Platform, including identity, secrets, workloads, APIs, containers, serverless services, and Kubernetes.
Working knowledge of threat modeling, secure architecture principles, software supply-chain security, SBOM/VEX concepts, artifact integrity, dependency governance, and provenance or attestation practices.
Ability to communicate technical risk clearly to developers, architects, executives, auditors, and non-technical stakeholders, and to translate findings into prioritized engineering actions.
Ability to lead through influence, exercise sound judgment under uncertainty, mentor others, and balance security outcomes with client and business needs.
Typically requires a Bachelor’s degree in computer science, software engineering, cybersecurity, information systems, or a related field and 5 or more years of relevant experience; an equivalent combination of education, training, and experience may be considered.
Typically requires 3 or more years of hands-on application security, product security, penetration testing, secure software development, or software security assessment experience.
Demonstrated experience developing security automation and integrating application security controls into CI/CD workflows.
Practical experience using AI-assisted capabilities for application vulnerability analysis, with evidence of validation, governance, and measurable improvement in security outcomes.

Nice To Haves

GIAC Web Application Penetration Tester (GWAPT), GIAC Certified Web Application Defender (GWEB), or comparable application security certification.
Offensive Security Web Expert (OSWE) or comparable advanced assessment certification.
AWS, Microsoft Azure, Google Cloud, Kubernetes, or DevSecOps certification relevant to the assigned environment.

Responsibilities

Lead application security engineering activities across web applications, APIs, mobile applications, cloud-native services, containers, and supporting platforms.
Embed security controls throughout the software development lifecycle (SDLC), including requirements, architecture, design, development, build, test, release, and post-production monitoring.
Design, implement, tune, and govern automated security testing in CI/CD pipelines, including static application security testing (SAST), dynamic application security testing (DAST), interactive application security testing (IAST), software composition analysis (SCA), secrets detection, infrastructure-as-code scanning, container image scanning, API security testing, and mobile application testing.
Develop reusable automation, integrations, and security-as-code using Python, PowerShell, JavaScript, shell scripting, APIs, webhooks, and pipeline platforms to reduce manual effort and improve control coverage.
Build automated workflows that normalize, correlate, enrich, deduplicate, prioritize, ticket, route, retest, and close application vulnerability findings across security tools and engineering systems.
Leverage AI-assisted application vulnerability analysis to summarize evidence, identify code-to-vulnerability relationships, propose test cases, prioritize likely exploit paths, explain findings to developers, and draft remediation guidance.
Evaluate and govern AI-assisted security capabilities for accuracy, privacy, data handling, prompt-injection resistance, model and supply-chain risk, reproducibility, auditability, and human oversight; measure false-positive, false-negative, and remediation-quality outcomes.
Perform manual and tool-assisted application and API security assessments, validate exploitability, eliminate false positives, create proof-of-concept evidence when appropriate, and provide clear, actionable remediation guidance.
Lead application threat modeling and architecture risk reviews using practical methods such as abuse cases, data-flow analysis, trust-boundary analysis, and attack-path modeling.
Partner with software engineers, architects, product owners, DevOps/platform teams, cloud teams, and risk stakeholders to translate security requirements into pragmatic engineering solutions.
Develop and maintain secure coding standards, reusable security patterns, guardrails, reference implementations, and developer enablement materials aligned with OWASP guidance and recognized industry practices.
Create risk-based service-level objectives and prioritization models that account for exploitability, reachability, business criticality, data sensitivity, compensating controls, threat intelligence, and exposure.
Define and report meaningful program metrics, including coverage, control adoption, vulnerability aging, recurrence, escape rate, mean time to remediate, automation effectiveness, and risk reduction.
Conduct root-cause analysis for recurring vulnerability classes and drive systemic prevention through framework changes, paved-road patterns, automated controls, and targeted education.
Serve as a technical escalation point for complex application vulnerabilities and major cybersecurity incidents; participate in an on-call rotation as required.
Mentor application security engineers and developers, contribute to technical strategy and roadmaps, and remain current with emerging attack techniques, defensive technologies, and AI-enabled software development risks.