Cybersecurity AI Forward Deployed Engineer - FDE - Consultant

Accenture•St. Louis, MO

1d•$54,400 - $235,100

About The Position

Accenture Security is hiring a Cybersecurity Forward Deployed Engineer to work as a production engineer embedded with clients’ security and engineering teams. The role focuses on making AI systems secure, governed, and resilient in complex production environments, with ownership of outcomes such as reducing attack surface, safely deploying AI, and improving security posture. The engineer works within Accenture’s Reinvention Delivery Engine (RDE) Pod, a small, persistent team aligned to a client business domain or AI program. Pods work in 90-day delivery cycles and own outcomes across building, deploying, and optimizing solutions while embedding in the client’s technology organization. Agentic coding is the primary delivery method, using tools such as Claude Code, Cursor, or GitHub Copilot to build security tooling, detection systems, threat models, and governance frameworks. The position supports security architecture and threat modeling for production agentic AI, delivers hands-on security engineering, manages AI-specific threat surfaces, and implements enterprise AI security controls and governance. It also contributes to client security strategy and reusable patterns for Accenture’s Secure AI practice. Travel may be required, ranging from 0% to 100% depending on business and client needs.

Requirements

At least 3 years of production engineering experience with cybersecurity depth in at least one area: AppSec, SecOps/detection engineering, cloud security, IAM, offensive security/penetration testing, or GRC.
At least 1 year of hands-on experience designing and deploying agentic AI solutions in a production environment; theoretical familiarity does not qualify.
At least 3 years working with cloud platform security fundamentals on AWS, Azure, or GCP, including IAM, network security, secrets management, and AI service security configurations.
Bachelor’s degree or equivalent, defined as a minimum of 12 years of work experience; alternatively, an associate degree with at least 6 years of work experience.
Ability to communicate security risk in business terms and translate threat exposure into risk-adjusted investment rationale for a CISO or CFO.
Ability to use agentic coding tools as the primary delivery environment; daily practice with Claude Code, Cursor, or GitHub Copilot is described.

Responsibilities

Support AI security architecture and threat modeling for production agentic deployments, including LLM systems, multi-agent pipelines, RAG architectures, and MLOps infrastructure, from assessment through hardened deployment.
Build AI-powered detection systems, automated threat-response tooling, security assessment frameworks, and governance automation using agentic coding tools such as Claude Code, Cursor, or GitHub Copilot.
Support AI threat-surface management at program scale, including OWASP LLM Top 10 controls, prompt-injection hardening, model-extraction prevention, adversarial-input defenses, and AI supply-chain security.
Architect and govern AI security controls across identity and access, data pipelines, model serving, and multi-system integrations on AWS, Azure, or GCP.
Assist with implementing AI governance frameworks, including the EU AI Act, NIST AI RMF, and model risk management in production systems.
Shape AI reinvention security strategy for client CISOs and CTOs, including risk-adjusted investment cases, security architecture roadmaps, and AI governance operating models aligned with commercial outcomes.
Define and publish reusable security patterns, playbooks, and accelerators for use across client engagements and to grow the Secure AI practice.
Contribute to architecture design sessions, threat-modeling workshops, and code-with sessions with client engineering and security leadership teams.
Work within an RDE Pod on end-to-end outcomes across build, deploy, and optimize during 90-day delivery cycles.