Application Security Engineer - Threat & Vulnerability Management (AI Focus)

Req ID: 389523

NTT DATA strives to hire exceptional, innovative and passionate individuals who want to grow with us. If you want to be part of an inclusive, adaptable, and forward-thinking organization, apply now.

We are currently seeking a Application Security Engineer - Threat & Vulnerability Management (AI Focus) to join our team in Bangalore, Karnātaka (IN-KA), India (IN).

Job Description – Application Security Engineer – Threat & Vulnerability Management (AI Focus)

Role: Application Security Engineer – Threat & Vulnerability Management Domain: Application Security, Vulnerability Management & Secure SDLC

Position Summary

We are seeking an experienced Application Security Engineer – Threat & Vulnerability Management (AI Focus) to drive secure-by-design engineering across AI applications, agentic AI solutions, code, and CI/CD pipelines.

The role will be responsible for integrating application security controls into the AI development lifecycle, managing vulnerabilities across AI workloads, and ensuring that AI agents and applications are designed, developed, tested, and deployed securely.

The ideal candidate will bring strong hands-on experience in Application Security, SAST, DAST, SCA, vulnerability management, threat modeling, Secure SDLC, DevSecOps, and CI/CD security, along with a good understanding of emerging security risks associated with LLMs, Generative AI, and Agentic AI systems.

Key Responsibilities

Application Security & Vulnerability Management

• Own and drive application vulnerability management for AI applications, agents, APIs, services, and associated workloads.

• Identify, assess, prioritize, track, and support remediation of security vulnerabilities throughout the development lifecycle.

• Establish vulnerability-management SLAs, severity classifications, remediation timelines, and escalation processes for AI workloads.

• Perform security reviews and vulnerability triage to distinguish exploitable risks from false positives and lower-priority findings.

• Partner with engineering teams to drive timely remediation of identified security issues.

SAST, DAST & Software Composition Analysis

• Integrate and manage SAST, DAST, and Software Composition Analysis (SCA) tools within agent and application CI/CD pipelines.

• Define security quality gates and automated security checks across build, test, and deployment processes.

• Analyze security findings generated by automated security tools and work with developers to remediate vulnerabilities.

• Continuously improve security tooling, automation, scanning coverage, and vulnerability-detection capabilities.

AI / LLM Security & Threat Modeling

• Lead threat modeling for AI applications, LLM-powered solutions, and autonomous/agentic AI systems.

• Assess AI solutions against industry frameworks such as the OWASP Top 10 for Large Language Model Applications and emerging Agentic AI security risks.

• Identify and mitigate AI-specific attack scenarios, including:

• Prompt injection

• Indirect prompt injection

• Tool/function abuse

• Excessive agency

• Sensitive information disclosure

• Data exfiltration

• Insecure output handling

• Supply-chain risks

• Improper access or privilege management

• Collaborate with AI engineers and architects to incorporate security controls into agent design and architecture.

AI/ML Software Supply Chain & SBOM

• Build, implement, and maintain AI/ML Software Bill of Materials (SBOM) capabilities.

• Identify and manage security risks associated with AI/ML libraries, frameworks, models, packages, dependencies, and third-party components.

• Establish processes for monitoring vulnerabilities across the AI/ML software supply chain.

• Support dependency governance and secure component-management practices.

Secure SDLC & DevSecOps

• Establish and enforce Secure Software Development Lifecycle (Secure SDLC) guardrails for AI development teams.

• Embed security controls throughout requirements, design, development, testing, deployment, and production operations.

• Integrate application security practices into DevSecOps pipelines.

• Partner with developers, AI/ML engineers, architects, platform teams, and security teams to implement security-by-design principles.

• Provide security guidance, standards, patterns, and reusable controls for AI application development teams.

Required Skills & Qualifications

• 7+ strong hands-on background in Application Security and Vulnerability Management.

• 5+ proven experience with SAST, DAST, and SCA tools, including security finding analysis and vulnerability triage.

• 5+ years of strong understanding of Secure SDLC, DevSecOps, and CI/CD security integration.

• 3 to 5 years of hands-on experience integrating automated security testing into development and deployment pipelines.

• Strong knowledge of application security vulnerabilities and common attack techniques.

• Experience conducting threat modeling for enterprise applications and distributed systems.

• Understanding of LLM, Generative AI, and Agentic AI security risks.

• Working knowledge of AI/LLM attack surfaces, including prompt injection, tool abuse, excessive agency, sensitive data exposure, and data exfiltration.

• Familiarity with OWASP LLM security guidance and emerging Agentic AI security frameworks.

• Knowledge of AI/ML supply-chain security and SBOM practices.

• Experience defining and enforcing vulnerability-management policies, SLAs, remediation processes, and security controls.

• Strong communication and collaboration skills with the ability to work across Security, AI/ML, Application Development, DevOps, Platform Engineering, and Architecture teams.

Preferred Qualifications

• Strong Python scripting and automation skills, particularly for security tooling and CI/CD integration.

• Experience building security automation and integrating security tools using APIs.

• Exposure to cloud-native application and AI security environments.

• Familiarity with securing APIs, microservices, containers, and cloud-based AI workloads.

• Experience working with enterprise AI/ML or Generative AI platforms.

• Relevant security certifications such as:

• GIAC Web Application Penetration Tester (GWAPT)

• Certified Secure Software Lifecycle Professional (CSSLP)

• Offensive Security Certified Professional (OSCP)

• Other relevant Application Security / Cloud Security certifications

About NTT DATA

NTT DATA is a $30 billion business and technology services leader, serving 75% of the Fortune Global 100. We are committed to accelerating client success and positively impacting society through responsible innovation. We are one of the world's leading AI and digital infrastructure providers, with unmatched capabilities in enterprise-scale AI, cloud, security, connectivity, data centers and application services. our consulting and Industry solutions help organizations and society move confidently and sustainably into the digital future. As a Global Top Employer, we have experts in more than 50 countries. We also offer clients access to a robust ecosystem of innovation centers as well as established and start-up partners. NTT DATA is a part of NTT Group, which invests over $3 billion each year in R&D.

Whenever possible, we hire locally to NTT DATA offices or client sites. This ensures we can provide timely and effective support tailored to each client’s needs. While many positions offer remote or hybrid work options, these arrangements are subject to change based on client requirements. For employees near an NTT DATA office or client site, in-office attendance may be required for meetings or events, depending on business needs. At NTT DATA, we are committed to staying flexible and meeting the evolving needs of both our clients and employees. NTT DATA recruiters will never ask for payment or banking information and will only use @nttdata.com, @nttdatafed.com and @talent.nttdataservices.com email addresses. If you are requested to provide payment or disclose banking information, please submit a contact us form, https://us.nttdata.com/en/contact-us.

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