AppSec & AI Security Manager

AI/ML Security RAG Pipelines, AI Governance, Secure MLOps, Adversarial ML Mitigation, LLM Guardrails

Position Summary :

We are seeking an AppSec & AI Security Architect to lead and deliver security architecture reviews and secure-by-design outcomes across modern applications, cloud-native platforms, APIs, third-party integrations, and AI-enabled systems. This role combines deep technical expertise with delivery leadershipoverseeing teams and engaging senior client stakeholders. For LLM, RAG, copilot, bot, and agentic systems, you will evaluate and mitigate risks such as prompt injection, sensitive data disclosure, insecure tool/function calling, excessive agency/permissions, insecure output handling, model and dependency supply-chain risks, training/fine-tuning data leakage, and vector database/embedding store weaknesses, and drive adoption of practical guardrails and governance.

As an AppSec & AI Security Manager, you will:

• Architect and oversee the build of AI agents and agentic workflows for security automation (e.g., AppSec triage agents, security copilots, autonomous remediation workflows, AI red-team automation).
• Lead end-to-end delivery of AppSec, DevSecOps, and AI Security (AISecOps) engagements—managing onshore/offshore engineers and architects across the lifecycle (assess, design, implement, operate).
• Define and drive adoption of secure-by-design architectures for modern applications, cloud-native platforms, and AI/agentic systems; establish reference architectures and reusable patterns.
• Review and approve security architecture for systems spanning microservices, APIs, distributed platforms, and AI/RAG/agentic solutions, including data flows, trust boundaries, secrets, encryption, and third-party dependencies.
• Establish reusable patterns for CI/CD pipeline security, policy-as-code, IaC scanning, and software/artifact integrity (e.g., SBOM and ML-BOM workflows), aligned to secure SDLC goals.
• Establish and assess container/Kubernetes security patterns (admission control, multi-tenant isolation, runtime protection) and supply-chain controls (e.g., SLSA, sigstore).
• Define and assess LLM/agent guardrails (prompt/output handling controls, grounding strategies, tool allow-listing, sandboxing, rate limits/quotas, and human-in-the-loop patterns) and verify effectiveness through testing.
• Drive LLM/agent security testing (abuse/misuse cases, prompt injection/jailbreak testing, tool-use abuse validation, adversarial evaluation) and ensure findings are translated into actionable mitigations and risk decisions.
• Define runtime monitoring and incident response requirements for AI systems (secure telemetry, privacy-aware prompt/output logging patterns, abuse detection, drift signals, containment/rollback playbooks).
• Shape clients’ enterprise AppSec and AISecOps programs—build roadmaps aligning security investment with business outcomes and regulatory requirements; define governance, metrics, and operating model.
• Serve as the primary day-to-day client interface—build rapport and trust with senior stakeholders (e.g., CISOs, CTOs, Heads of AI/Engineering) and guide prioritization and decision-making.
• Oversee the quality of project deliverables—assessment reports, architectures, threat models, runbooks, and risk/security recommendations.
• Support business development: define scope, build estimates and pricing, package proposals, and support proposal presentations.
• Contribute to eminence—whitepapers, points-of-view, conference content—on the convergence of AppSec, DevSecOps, and AISecOps.
• Lead talent processes—recruiting, coaching, performance management, and capability building for AppSec and AI Security professionals.

Qualifications

Must-have skills / project experience / certifications (AI Security Focus)

• 6+ years of progressively responsible experience in application security, DevSecOps, product security, and/or security architecture, with increasing scope and ownership; including 2+ years in consulting, project leadership, or client-facing delivery.
• Strong knowledge of application, API, IAM, data, and cloud security architecture (authn/authz, encryption, key management, secrets, network trust boundaries, logging/monitoring, resiliency, and third-party dependencies).
• Hands-on experience designing or building AI agents/agentic workflows and securing **LLM-enabled applicat…