Senior Security Engineer - Secure SDLC

Company :

enGen

Job Description :

JOB SUMMARY

***CANDIDATE MUST BE US Citizen (due to contractual/access requirements)***

Highmark Health is seeking a Senior Security Engineer to join our Enterprise Application Security team and play a pivotal role in shaping how security is built into our software — not bolted on after the fact.

This is a high-impact, engineering role for a security professional who is passionate about preventing vulnerabilities before they happen. You will be at the forefront of our shift-left security strategy , working directly alongside our engineering teams to embed security into every stage of the software development lifecycle — from the first line of code to production deployment.

If you thrive at the intersection of security engineering & architecture , developer enablement & collaboration , and automation , and you want to build something that matters at enterprise scale in one of the nation's leading health and insurance organizations — this role is for you.

Build & Enforce Shift-Left Security Controls

• •Design and implement security guardrails that catch vulnerabilities at the earliest possible point in the development process, including within AI-assisted development workflows, IDEs, at commit time, and within CI/CD pipelines.
• •Configure and enforce pipeline security gates across the enterprise, ensuring code, AI-generated code, infrastructure-as-code, and deployment artifacts cannot advance to production without meeting defined security standards.
• •Deploy and manage application security scanners , including SAST, Dependency Scanning, Container Scanning, Secret Detection, DAST, API Security Testing, and emerging AI/LLM security assessment capabilities across the enterprise development platform.
• •Develop security-as-code policies and enforcement rules that scale across a large, distributed engineering organization.
• •Partner with Software Delivery Enablement teams to establish security controls, governance requirements, and safe usage patterns for AI coding assistants, AI agents, and AI-enabled developer tooling.

Drive Vulnerability Risk Reduction

• •Lead risk-based triage and prioritization of detected vulnerabilities , leveraging exploitability signals such as EPSS scores, Known Exploited Vulnerability (KEV) status, reachability analysis, and emerging AI-specific risk indicators.
• •Establish and track remediation SLAs aligned to vulnerability severity and business risk, with a focus on eliminating Critical and High findings before they reach production.
• •Identify and remediate security risks associated with AI-generated code, AI-enabled applications, model integrations, prompt injection vulnerabilities, insecure agent behaviors, and exposure of sensitive data to AI platforms.
• •Conduct root cause analysis on recurring vulnerability patterns and drive systemic improvements through tooling, standards, secure development practices, and developer education.
• •Monitor and report on key security health metrics including Mean Time to Remediate (MTTR) , security debt trends, pre- versus post-production detection rates, and AI security risk reduction metrics.

Automate & Optimize the Security Toolchain

• •Architect and maintain the enterprise application security toolchain , ensuring tools are properly integrated, tuned, and delivering high-fidelity, actionable signal.
• •Evaluate, onboard, and operationalize emerging security technologies that improve visibility and governance over AI-assisted software development and software supply chains.
• •Build automation workflows for vulnerability triage, escalation, assignment, and reporting, reducing manual overhead and accelerating response times.
• •Continuously optimize scanner configurations to minimize false positives and maximize detection accuracy.
• •Develop dashboards and reporting pipelines that give engineering and security leadership real-time visibility into application security posture, AI security adoption , and policy compliance.
• •Integrate security controls and monitoring into approved AI development platforms, coding assistants, model gateways, and agentic development workflows.

Enable & Empower Developers

• •Serve as a trusted, embedded security advisor to engineering teams, providing hands-on guidance, code review support, AI security consultation, and practical remediation recommendations.
• •Design and deliver security training, workshops, and reference materials that make secure coding, secure AI development, and responsible use of AI coding assistants accessible and actionable for developers at all levels.
• •Build and grow a Security Champions program , embedding security advocates within engineering teams to extend the AppSec program's reach across the organization.
• •Create and maintain secure coding standards, secure AI development standards, design patterns, and reusable security libraries that reduce security burden on development teams.
• •Develop guidance and reference architectures for secure implementation of LLMs, AI copilots, agentic workflows, model integrations, and AI-enabled business applications.
• •Partner with development, architecture, and platform teams to embed secure-by-default AI development practices throughout the SDLC.

Measure, Report & Continuously Improve

• •Define, track, and report on AppSec KPIs that demonstrate program effectiveness and drive continuous improvement.
• •Establish and report on AI security metrics such as AI tooling adoption, policy compliance, AI risk assessments completed, AI-generated code review coverage, and identified AI-related security findings.
• •Conduct regular security posture reviews and present findings, trends, and recommendations to engineering and security leadership.
• •Support audit, risk, and compliance activities by ensuring security controls, AI governance requirements , and secure development standards are documented, measurable, and consistently enforced.
• •Benchmark program maturity against industry frameworks such as OWASP SAMM, BSIMM, OWASP Top 10 for LLM Applications , and emerging AI security best practices, driving year-over-year improvement.
• •Continuously assess emerging threats, vulnerabilities, and attack techniques affecting modern software delivery pipelines, software supply chains, and AI-enabled applications.

Assist in AI Application Security & Governance

• •Assist with security reviews and threat modeling for AI-enabled applications, LLM integrations, AI agents, and AI-assisted development platforms.
• •Collaborate with Security Architecture to recommend and establish technical controls and guardrails supporting enterprise AI governance requirements.
• •Evaluate security risks associated with AI models, prompts, training data, model supply chains, MCP integrations, and agentic workflows.
• •Partner with Architecture, ISRM, and Software Delivery Enablement teams to define secure AI development standards and implementation patterns across the enterprise.

Preferred Qualifications

• •Experience with GitLab Ultimate security features including Vulnerability Reports, Security Policies, Compliance Frameworks, and security controls supporting AI-assisted development workflows.
• •Deep proficiency with application security scanning tools including SAST, DAST, SCA/Dependency Scanning, Container Scanning, Secret Detection, API Security Testing , and emerging AI application security assessment capabilities.
• •Deep proficiency with JFrog security and compliance tools such as Xray and Curation , including Policies, Watches, Impact Analysis, Software Supply Chain controls, and reporting.
• •Familiarity with threat modeling methodologies such as STRIDE, PASTA , and their application to AI-enabled systems, LLM integrations, and agentic workflows.
• •Working knowledge of common AI security risks including prompt injection, insecure output handling, excessive agency, retrieval risks, model poisoning, training data exposure, sensitive data leakage, and model supply chain threats.
• •Experien