Secure AI Innovation Engineer (regular/senior) (She/He/They)

Accenture is a leading global professional services company with approximately 791,000 people serving clients in more than 120 countries. The company helps businesses, governments, and organizations build their digital core, optimize operations, accelerate revenue growth, and enhance citizen services. Accenture combines technology strength and leadership in cloud, data, and AI with industry experience, functional expertise, and global delivery capability.

• Strong interest in cybersecurity, innovation, and modern technologies, with a willingness to learn, grow, and take ownership of security topics.
• Background in IT, software development, cloud engineering, or security, with foundational knowledge across applications and infrastructure.
• Understanding of end-to-end system architectures, including monolithic, microservices, event-driven, and cloud-native designs.
• Foundational knowledge of Application Security, including OWASP Top 10, OWASP ASVS, secure coding principles, and common attack techniques (e.g., XSS, SQLi, CSRF).
• Basic to intermediate understanding of Cloud Security concepts, including cloud shared responsibility model, Identity and Access Management, network and data security in cloud environments.
• Familiarity with SSDLC principles, including security requirements, secure design, testing, and vulnerability management.
• Interest in AI, LLMs, and Agentic AI, with awareness of associated security challenges.
• Ability to communicate clearly with both technical and non-technical stakeholders.
• Ownership mindset, curiosity, and ability to work independently while collaborating with teams.
• Basic experience or knowledge of one major cloud platform (Azure, AWS, or GCP), CI/CD pipelines and DevSecOps concepts, REST APIs and API security fundamentals.
• Scripting or programming skills in at least one language (e.g., Python, Java, JavaScript, C#, Go).

Bonus points:

• Experience in Application Security or Cloud Security roles.
• Understanding of AI system architectures, including data pipelines, model lifecycle, and inference services.
• Experience or curiosity around AI-driven security tools and automation.
• Industry certifications (cloud or security) are a plus but not required.
• Exposure to container platforms (Docker, Kubernetes), CNAPP, CSPM, SIEM/SOAR, or cloud-native security tooling, security automation and orchestration.

• Support clients in increasing their cybersecurity maturity by assessing current security posture, identifying gaps, and defining pragmatic improvement roadmaps across applications, cloud platforms, and development processes.
• Act as a trusted security advisor, proposing modern and innovative security solutions that improve security posture, enable automation, increase operational efficiency, and enhance the effectiveness and quality of security teams.
• Design and implement end-to-end security controls covering applications, cloud infrastructure, development pipelines, and operational environments.
• Enable secure adoption of AI technologies, including LLM-based solutions and Agentic AI, by designing and securing client environments to support safe and responsible AI usage.
• Define and implement security guardrails for AI environments, identifying, assessing, and mitigating AI-specific security risks such as prompt injection, data leakage, data poisoning, model abuse, insecure integrations, and misuse of autonomous AI agents.
• Collaborate with development, architecture, and platform teams to embed security into SDLC and SSDLC, following security-by-design and shift-left principles.
• Support and improve secure development environments, including CI/CD pipelines, Infrastructure-as-Code, APIs, and cloud-native platforms.
• Perform or support application and system security assessments aligned with industry standards such as OWASP Top 10, OWASP ASVS, and OWASP API Top 10.
• Conduct or facilitate threat modeling for end-to-end solutions, including cloud-native, hybrid, distributed, and AI-enabled architectures.
• Design and support secure cloud and hybrid architectures across Azure, AWS, or GCP, covering identity, network security, data protection, and platform security.
• Support containerized and cloud-native environments (e.g., Kubernetes-based platforms) by ensuring secure configuration, posture management, and workload protection.
• Leverage automation and AI-powered security tools to improve vulnerability detection, code and configuration analysis, threat detection, and security operations efficiency.
• Translate complex security risks into clear, actionable recommendations for technical and non-technical stakeholders.
• Support clients in building long-term security capabilities, enabling secure digital transformation and responsible use of AI technologies.
• Secure cloud foundations, data flows and pipelines, AI model integration and inference APIs, Identity and Access Management.

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