Security Software Engineer (AppSec & AI-Assisted Development) (Teradyne, India)

Overview Teradyne Information Security is building an in-house capability to design, build and operate its own security applications using AI-assisted development. The Security Software Engineer owns those applications end to end: architecture, delivery, bug resolution, new features and the full application security lifecycle. The first major platform in scope is the Cyber Incident Response Platform, with further security applications to follow.

Applications built with AI coding tools can be delivered quickly, but speed is only valuable if the result is secure, tested, maintainable and trusted by the teams who depend on it. This role is accountable for that outcome. The engineer is expected to treat AI-generated code as untrusted input, applying engineering discipline and application security rigor to everything that ships, and to set the standard for how Information Security builds software with AI.

The platforms in this role will hold some of the most sensitive data in the company, including incident details, investigation evidence and security telemetry. They must therefore meet a higher security bar than a typical internal tool.

This role reports into the Information Security team led by the Deputy CISO and works closely with Security Operations and Incident Response, Product Security, Enterprise Architecture and IT. It suits a hands-on engineer who combines software development skills with genuine application security depth, and who has a background or strong interest in cyber security or product security.

**Responsibilities** **Building and Owning Security Applications**

• Application Delivery — Design, build and deliver security applications using AI-assisted development, beginning with the Cyber Incident Response Platform. Translate requirements from Incident Response, Security Operations and Product Security into working, maintainable software.
• End-to-End Ownership — Own each application across its lifecycle, including architecture, roadmap, backlog, releases, documentation and eventual retirement. Act as the accountable owner and technical point of contact for the platforms you build.
• Maintenance and Support — Triage and resolve bugs, respond to production issues, and design and deliver new features and enhancements. Keep the applications reliable, performant and aligned to agreed service levels.
• Integration — Build and maintain secure integrations with SIEM, SOAR, EDR, ticketing, identity and data platforms using least-privilege service identities and well-governed APIs.
• Quality Engineering — Establish automated testing, code review, CI/CD and observability so that AI-generated code is verified rather than trusted, and so that changes can be released with confidence.

**Owning the Application Security Lifecycle**

• Secure SDLC — Define and operate the secure development lifecycle for the applications you own, including secure design, threat modeling, secure coding standards and pipeline security gates.
• Security Testing and Vulnerability Management — Run SAST, DAST, software composition analysis, secrets scanning and infrastructure-as-code scanning in the pipeline. Triage and remediate findings within defined timelines and track remediation to closure.
• Dependency and Supply Chain Security — Manage third-party dependencies, SBOMs, version pinning and package provenance. Defend against hallucinated, typosquatted or malicious packages introduced by AI tooling.
• Application Hardening — Implement strong authentication and authorization (including role-based access), secrets management, encryption, audit logging, data retention, and evidence integrity appropriate to incident response data.
• Independent Assurance — Support independent review of the applications you build through Product Security, peer review and third-party penetration testing. Remediate findings and maintain evidence to support internal and external audits.

**Secure AI-Assisted Development**

• Engineering Standards — Define, apply and continuously improve standards for AI-assisted development, including spec-driven workflows, mandatory human review of AI-generated code, test coverage expectations, approved tools and models, and prompt and context hygiene (no secrets or sensitive data in prompts).
• Securing the Tooling — Secure the coding agents and development environment, including scoped permissions, sandboxing, controls on MCP and tool access, and defenses against prompt injecti…