Senior Product Security Engineer – AI, DevSecOps

McKesson

. Conduct security architecture reviews, threat modeling, and security assessments for applications, APIs, cloud services, and AI-enabled systems .

Posted 9/23/2026full-timeRemote • United StatesSenior💰 $140,300 - $233,800 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in security architecture, threat modeling, and vulnerability remediation, with a strong focus on integrating security practices into the software development lifecycle. Proficient in securing AI and cloud-native applications while implementing DevSecOps practices and automation solutions.

Highest-signal resume keywords

Security Architecture ReviewsDevSecOps PracticesAI/ML SecurityInfrastructure-as-CodeVulnerability Management

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Threat ModelingSecure Software Development LifecycleScripting (Python, PowerShell, Bash)Security Tooling (SAST, DAST, SCA)Cloud Security (Microsoft Azure, AWS, Google Cloud)CI/CD Technologies (Terraform, GitHub Actions, Azure DevOps)Policy-as-CodeCompliance-as-CodeSecurity AutomationIncident Response

Soft Skills

Communication of Technical RisksInfluencing Across Teams

Tools & Technologies

KubernetesContainersSecurity Orchestration, Automation, and Response (SOAR)Cloud-Native Technologies

Industry Keywords

NIST Cybersecurity FrameworkSOC 2HIPAASOXISO 27001OWASP Top 10 for LLM Applications

Tech Stack

Tools & technologies

AWSAzureCloudCyber SecurityGoogle Cloud PlatformJenkinsKubernetesPythonTerraform

About the role

Key responsibilities & impact

Conduct security architecture reviews, threat modeling, and security assessments for applications, APIs, cloud services, and AI-enabled systems
Define and implement security requirements, standards, reusable design patterns, and security guardrails across the software development lifecycle
Identify, assess, and help mitigate security risks while partnering with engineering teams to remediate vulnerabilities and strengthen secure coding practices
Assess and secure AI, machine learning, and generative AI solutions, including controls for model governance, secure access, data protection, and AI risk management
Design and implement security controls for cloud-native applications, containers, Kubernetes, serverless platforms, and infrastructure-as-code deployments
Integrate security controls and automated testing into CI/CD pipelines, including SAST, DAST, software composition analysis, secrets detection, container scanning, and infrastructure scanning
Develop security automation using scripting, infrastructure-as-code, policy-as-code, and compliance-as-code technologies
Support identity and access management, secrets management, cloud security monitoring, vulnerability management, and incident response activities
Contribute to compliance initiatives, security metrics, risk reporting, and continuous improvement efforts
Provide technical guidance on secure development practices and champion a security-first engineering culture
Partner with engineering, platform, architecture, and product teams to design secure solutions and strengthen application and AI-system security posture

Requirements

What you’ll need

Degree in Computer Science, Information Security, Engineering, or related technical field, or equivalent experience
Typically requires 7+ years of relevant experience in application security, product security, security engineering, or a related cybersecurity discipline
Experience conducting security architecture reviews, threat modeling, secure design reviews, and vulnerability remediation
Experience implementing DevSecOps practices and integrating security controls into CI/CD pipelines
Experience securing AI/ML platforms, generative AI solutions, large language model applications, or data science workflows
Experience with secure software development lifecycle practices, vulnerability management, and Agile development methodologies
Experience with Microsoft Azure, AWS, or Google Cloud Platform and cloud-native technologies including containers and Kubernetes
Experience with infrastructure-as-code and CI/CD technologies such as Terraform, GitHub Actions, Azure DevOps, GitLab, Jenkins, or similar tools
Proficiency in scripting and automation using Python, PowerShell, Bash, or comparable languages
Experience with security tooling including SAST, DAST, software composition analysis (SCA), container security, secrets detection, and infrastructure scanning
Knowledge of AI security frameworks and controls, including the OWASP Top 10 for LLM Applications and the NIST AI Risk Management Framework
Experience implementing policy-as-code, compliance-as-code, or security automation solutions
Experience supporting regulatory and control frameworks such as SOC 2, HIPAA, SOX, NIST Cybersecurity Framework (CSF), or ISO 27001
Experience with Security Orchestration, Automation, and Response (SOAR) platforms
Ability to communicate complex technical risks and tradeoffs to both technical and non-technical stakeholders
Ability to influence across teams, contribute to technical standards, and promote secure engineering practices

Benefits

Comp & perks

Competitive compensation package
Annual bonus or long-term incentive opportunities may be offered
Equal employment opportunities
Reasonable accommodation for job search or application