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Cybersecurity AI Forward Deployed Engineer - FDE - Consultant
Job in
Boston, Suffolk County, Massachusetts, 02297, USA
Listed on 2026-10-15
Listing for:
Accenture
Full Time
position Listed on 2026-10-15
Job specializations:
-
IT/Tech
Cybersecurity, AI Engineer (Applied/Software)
Job Description & How to Apply Below
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. Do you have the deep functional and technical experience to help implement security solutions that align with our clients’ business objectives?
Do you have the expertise to design and deliver solutions for establishing system user's credentials, and processes for applying those credentials to access enterprise systems and applications?
You Are 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 Git Hub 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.
The Work Cybersecurity FDEs operate as part of Accenture’s Reinvention Delivery Engine (RDE) Pod: a small, persistent, outcome-oriented team aligned to a client’s business domain or AI programme. The pod operates in 90-day delivery cycles, owns end-to-end outcomes across build, deploy, and optimize, and embeds directly inside the client’s technology organization.
Key Responsibilities Support 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 Git Hub Copilot in daily delivery practice
Support 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 work streams
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)
Assist with AI governance framework implementation: EU AI Act, NIST AI RMF, and model risk management applied to live…
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