Cybersecurity Forward Deployed Engineer - FDE - Manager

Accenture•Atlanta, GA

3d•$80,400 - $305,000•Hybrid

About The Position

Accenture Security helps organizations prepare, protect, detect, respond, and recover along with all points of the security lifecycle. Cybersecurity challenges are different for every business in every industry. Leveraging our global resources and advanced technologies, we create integrated, turnkey solutions tailored to our client's needs across their entire value chain. Whether we’re defending against known cyberattacks, detecting and responding to the unknown, or running an entire security operations center, we will help companies build cyber resilience to grow with confidence. Our team of the security sector’s brightest people uses the coolest tech to out-hack the hackers and help clients build resilience from within. We blend risk strategy, digital identity, cyber defense, application security, and managed service solutions to rethink the entire security lifecycle. This is not a security consulting role. It is not a compliance advisory position. It is not a pen test engagement. A Cybersecurity Forward Deployed Engineer is a production engineer who works embedded inside a client’s enterprise—shoulder to shoulder with their security and engineering teams—to make AI systems secure, governed, and resilient in real, complex organizational environments. You own outcomes: reduced attack surface, production-safe AI deployments, measurable security posture improvement. Agentic coding is not a supporting skill for this role—it is the primary method of delivery. You use Claude Code, Cursor, or GitHub Copilot as your standard operating environment. You build security tooling, detection systems, threat models, and governance frameworks with AI co-authoring the code alongside you. Engineers who treat AI tools as optional accelerators are not the profile. Engineers who cannot deliver without them are.

Requirements

Minimum of 8 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 1 year of hands-on experience designing and deploying agentic AI solutions in a production environment—non-negotiable; theoretical familiarity does not qualify
Minimum 6 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 6 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
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

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