Accenture

AI- Cybersecurity Forward Deployed Engineer- FDE Senior Manager

Rol remoto de Cybersecurity Engineering con fit claro de ubicación del candidato.

Publicado14 jul 2026

Países elegibles41 países aceptados

Señal de seniorityLead

Modelo de trabajoRemoto

Ubicaciones aceptadas para candidatos

AlbaniaArmeniaAustriaBielorrusiaBélgicaBulgaria+35 más

AWS Azure GCP LLM

Resumen del rol

AI- Cybersecurity Forward Deployed Engineer- FDE Senior Manager

Requisitos y responsabilidades

Contenido del rol extraído en secciones para revisar más rápido.

Key Responsibilities

Lead AI security architecture and threat modeling for production agentic deployments across complex multi-stakeholder client environments—LLM systems, multi-agent pipelines, RAG architectures, and MLOps infrastructure—owning the full security design from assessment through hardened deployment
Deliver hands-on security engineering using agentic coding tools as the primary build environment: build AI-powered detection systems, automated threat response tooling, security assessment frameworks, and governance automation using Claude Code, Cursor, or GitHub Copilot in daily delivery practice
Own AI-specific threat surface management at programme scale: OWASP LLM Top 10 controls, prompt injection hardening, model extraction prevention, adversarial input defences, and AI supply chain security across concurrent client workstreams
Architect and govern AI security controls across the enterprise stack: identity and access for AI systems, data pipeline security, model serving security, and multi-system integration risk across cloud platforms (AWS, Azure, or GCP)
Lead AI governance framework implementation: EU AI Act, NIST AI RMF, and model risk management applied to live production systems, not theoretical compliance exercises
Shape AI reinvention security strategy for client CISO and CTO: build risk-adjusted investment cases, security architecture roadmaps, and AI governance operating models aligned to commercial outcomes
Define and publish reusable security patterns, playbooks, and accelerators that scale across multiple client engagements and grow the Secure AI practice
Lead architecture design sessions, threat modeling workshops, and code-with sessions with client engineering and security leadership teams
Cybersecurity domain expertise in at least one discipline (AppSec, SecOps, IAM, cloud, GRC, or offensive security)
Proposal and SOW development, solution shaping, and client commercial engagement
Executive workshop facilitation and C-suite / CISO-level communication
Structured analytical thinking and hypothesis-driven problem decomposition
Team leadership: developing and coaching managers and consultants through delivery

Here's what you need

Minimum of 10 years of engineering experience in production environments with a cybersecurity discipline depth in at least one area: AppSec, SecOps / detection engineering, cloud security, IAM, offensive security / penetration testing, or GRC
Minimum 2 year of hands-on experience designing and deploying agentic AI solutions in a production environment—non-negotiable; theoretical familiarity does not qualify
Minimum 8 years of demonstrated end-to-end security delivery ownership experience in a client-embedded or production environment; internal advisory or compliance-only roles do not qualify
Minimum 8 years working with Cloud platform security fundamentals across at least one provider (AWS, Azure, or GCP): IAM, network security, secrets management, and AI service security configurations
Bachelor's degree or equivalent (minimum 12 years) work experience. (If Associate’s Degree, must have minimum 6 years work experience

Professional Skills Requirements

Proven ability to communicate security risk in business terms: can translate threat exposure into risk-adjusted investment rationale a CISO or CFO would act on
People lead responsibilities: experience managing, developing, and performance-managing a team of engineers; setting individual development plans and conducting career conversations

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