Senior Application & AI Security Engineer

KSM (Katz, Sapper & Miller)•Indianapolis, IN

About The Position

The Senior Application & AI Security Engineer is a hands-on individual contributor responsible for improving the security of KSM’s internally developed applications, APIs, AI-enabled solutions, and supporting integrations. The role partners with software developers, architects, and engineering teams to identify vulnerabilities, strengthen application designs, and implement and validate practical fixes. It integrates security into application and AI development workflows, with particular attention to authentication, authorization, sensitive client data, and risks associated with large language models (LLMs), AI agents, and connected tools. Application and API security are the primary focus. The engineer assesses and tests applications and APIs, reviews source code and architectures, works with developers to remediate and validate findings, and integrates practical security requirements and testing into development workflows and CI/CD pipelines. The role also reviews AI-enabled applications and integrations for risks such as prompt injection, sensitive data disclosure, insecure tool execution, and excessive permissions; supports related investigations; and communicates material risks and remediation progress to leadership.

Requirements

6+ years of hands-on experience in software development, application security engineering, or closely related technical roles.
Strong knowledge of web application and API security, including authentication, authorization, session management, and access control.
Demonstrated experience identifying, remediating, and validating vulnerabilities in production applications.
Ability to read, understand, and review application source code and collaborate directly with software developers.
Practical experience with application security assessments, manual testing, and code reviews.
Familiarity with OWASP Top 10, OWASP API Security Top 10, and OWASP Top 10 for LLM Applications.
Understanding of LLM application architectures, AI agent permissions, prompt injection, and the security implications of AI tool integrations.
Experience securing REST APIs, service-to-service authentication, and application credentials.
Experience incorporating security testing into CI/CD pipelines and software development workflows.
Ability to work independently, prioritize effectively, and explain technical risks and remediation to developers and business stakeholders.

Nice To Haves

Professional software development experience in languages and frameworks used by KSM’s development teams.
Experience securing applications and APIs hosted in AWS or Azure.
Experience with SAST, DAST, software composition analysis, and secrets-scanning tools.
Experience with application penetration testing, threat modeling, or secure architecture reviews.
Experience protecting sensitive financial, personal, or confidential client information in multi-tenant applications.
Familiarity with OAuth 2.0, OpenID Connect, SAML, and modern identity integration patterns.
Experience improving application/API logging, monitoring, and detection capabilities, including AI integrations.
Hands-on experience securing production AI applications or integrations, including AWS Bedrock or comparable platforms.
Experience with AI security testing, adversarial evaluation, or assessing prompt injection and agent tool access.
Relevant credentials such as OSWE, CSSLP, or GWAPT, or equivalent demonstrated expertise.

Responsibilities

Assess internally developed applications and APIs for vulnerabilities involving authentication, authorization, access control, and sensitive data exposure.
Perform hands-on application security testing, source code reviews, and architecture assessments; prioritize findings by exploitability and business impact.
Partner with developers to design and implement fixes, then independently validate that identified vulnerabilities are resolved.
Evaluate legacy applications, integrations, and application-to-application communications for security weaknesses.
Improve API protections, including service authentication, object-level authorization, secrets management, rate limiting, and abuse prevention.
Integrate practical security requirements and testing into the software development lifecycle and CI/CD pipelines.
Implement and maintain appropriate SAST, DAST, software composition analysis, and secrets-scanning capabilities.
Establish secure coding guidance and reusable patterns for traditional and AI-assisted development; review significant changes before production release.
Review application and AI architectures across AWS, Azure, and development environments, including client-data isolation and access to sensitive information.
Work with Security and Development to strengthen application, API, and AI integration logging, monitoring, and detection of unauthorized activity.
Assess AI-enabled applications, LLM integrations, and agent workflows for prompt injection, sensitive data disclosure, insecure tool execution, and excessive permissions.
Review how AI systems access, process, and transmit client information; evaluate controls for AWS Bedrock and other approved AI services.
Develop practical security testing for AI features, including adversarial tests of LLM integrations and agent tool calls.
Support investigations involving application or AI integration compromise and drive corrective actions to prevent recurrence.
Track application security findings through remediation and communicate material risks, priorities, and progress to leadership.