About the Role:

Grade Level (for internal use):

The role: AI Application Security Architect

Location: New Jersy, US or London, UK

Role Summary
We are seeking a highly skilled professional to drive the secure development lifecycle (SDLC) of agentic AI systems and applications across multi-cloud (AWS, Azure, GCP) and hybrid/on-prem environments. This role will focus on embedding robust application security controls, performing secure SDLC reviews, and leading the design and automation of security validation for agentic AI and LLM-powered solutions. The ideal candidate blends hands-on security engineering expertise with practical experience in AI/ML, MLOps/LLMOps, and secure application architecture, delivering resilient, compliant, and business-aligned agentic AI systems.

Key Responsibilities

Application Security & SDLC Automation

• Integrate security best practices throughout the SDLC for agentic AI applications, from design and code to deployment and operations.
• Develop and maintain automated security testing pipelines (SAST, DAST, SCA) for AI agents, APIs, and orchestration layers.
• Conduct security code reviews and threat modeling for agentic AI, focusing on model inputs/outputs, agent-to-agent (A2A), agent-to-process (A2P), and multi-agent control plane (MCP) interactions, as well as plugin/tool integration.
• Implement and automate security controls for secure agent deployment (sandboxing, RBAC/ABAC, policy enforcement, prompt injection/jailbreak mitigations).
• Ensure traceability and compliance by mapping agentic AI controls to regulatory frameworks (e.g., SOC 2, ISO 27001, NIST 800‑53, GDPR/CCPA).

Agentic AI Security Engineering

• Design, implement, and continuously improve security for agentic AI systems, including secure orchestration protocols such as A2A, A2P, MCP, and related agentic communication and coordination patterns.
• Build and test secure-by-design agentic AI features, including runtime isolation, egress controls, audit trails, and observability (telemetry, prompt/result logging, risk scoring).
• Embed LLMOps/MLOps security into CI/CD (model artifact scanning, SBOMs, policy-as-code, attestation, controlled promotion).
• Continuously evaluate agent safety with adversarial prompts, scenario-based testing, drift/hallucination detection, and bias/fairness assessments.

AI Security Testing Strategy & Pentesting

• Develop and execute a comprehensive AI security and penetration testing strategy for agentic AI applications and systems, with a focus on protocol-level security for A2A, A2P, MCP, and other agentic communication patterns.
• Lead offensive security assessments, including adversarial prompt testing, agent misuse scenarios, and vulnerability identification in agentic AI deployments.
• Collaborate with engineering teams to remediate findings and strengthen security posture across AI-enabled applications.

Governance, Stakeholder Enablement & Metrics

• Define and operationalize agentic AI security policies, standards, and playbooks for engineering teams, including secure usage of agentic protocols (A2A, A2P, MCP, etc.).
• Lead secure SDLC and AI Security enablement: deliver secure coding guidelines, threat modeling workshops, and prompt hygiene training.
• Effectively communicate risk, security posture, and value trade-offs to business stakeholders and executives.
• Present security metrics, dashboards, and reports on application/AI security KPIs, incidents, and risk reduction to both technical and non-technical audiences.
• Partner with Cloud, Data Science, and Platform teams to deliver secure agentic AI features while maintaining a strong security posture.

Required Qualifications

• 10+ years in Application Security or Security Engineering.
• 5+ years in secure SDLC roles.
• 1+ year in AI/ML or LLMOps security.
• Hands-on multi-cloud experience (AWS/Azure/GCP/OCI) with IAM, KMS, security monitoring, and AI services.
• Proficiency in secure SDLC automation tools (e.g., SAST, DAST, SCA, IaC scanning).
• Strong knowledge of agentic AI/LLM stacks (RAG, vector DBs, agent orchestration, prompt engineering, policy guardrails), with hands-on experience in agentic protocols such as A2A, A2P, MCP, and related patterns.
• Experience with threat modeling, offensive testing, and application security for AI/ML systems.
• Understanding of privacy and compliance requirements for AI-enabled applications.

Preferred Qualifications

• Experience deploying agentic AI or LLM-based applications with secure toolchains and runtime isolation.
• Familiarity with confidential computing, privacy-preserving ML, a