Security Architect
Project Role : Security Architect
Project Role Description : Define the cloud security framework and architecture, ensuring it meets the business requirements and performance goals. Document the implementation of the cloud security controls and transition to cloud security-managed operations.
Must have skills : Secure AI
Good to have skills : NA
Minimum 5 year(s) of experience is required
Educational Qualification : 15 years full time education
Summary:
Seeking a forward-thinking professional with an AI-first mindset to design, develop, and deploy enterprise-grade solutions using Generative and Agentic AI frameworks that drive innovation, efficiency, and business transformation.
Hands-on and technically strong AI Cloud Security Engineer with 6+ years of experience and deep expertise in cloud security, application security, threat modeling, and security assessments for AI-enabled and cloud-native systems. This role is ideal for professionals who enjoy working closely with engineering, DevSecOps, architecture, and platform teams to identify design gaps, strengthen security controls, and improve resilience across modern AI/ML and GenAI ecosystems. You will assess, design, and enhance security controls for cloud platforms, LLM-based applications, model hosting environments, APIs, RAG pipelines, and agentic workflows, while helping secure deployments across AWS, Azure, and GCP. The ideal candidate demonstrates strong practitioner depth, sound security architecture understanding, and a practical awareness of AI-specific risks such as prompt injection, jailbreaks, insecure orchestration, data leakage, model scraping, API abuse, and over-privileged access in AI pipelines.
Roles & Responsibilities
Lead AI-driven solution design and delivery by applying GenAI and Agentic AI to address complex business challenges, automate processes, and integrate intelligent insights into enterprise workflows for measurable impact.
Lead security architecture reviews for AI-enabled and cloud-native platforms to identify design gaps, trust boundary issues, and control weaknesses across enterprise environments.
Perform end-to-end threat modeling and security risk assessments for AI, GenAI, and cloud environments, covering applications, APIs, data flows, infrastructure, orchestration services, and integrations.
Assess security risks across AI/ML and GenAI use cases, including LLM-based applications, inference services, RAG pipelines, and agentic workflows.
Review cloud deployments on AWS, Azure, and GCP to validate secure configuration of IAM, networking, storage, compute, containers, Kubernetes, serverless services, logging, and encryption.
Evaluate security of APIs, gateways, and microservices, including authentication, authorization, token handling, rate limiting, throttling, and input validation.
Assess AI-specific threats such as prompt injection, jailbreaks, over-privileged tool access, data leakage, model misuse, insecure orchestration patterns, and unsafe trust boundaries.
Review the security design of AI pipelines, including data ingestion, preprocessing, embeddings, vector stores, training, inference, and monitoring components.
Secure GPU-based AI workloads and model hosting environments by validating access controls, workload protection, isolation boundaries, and monitoring coverage.
Assess and guide implementation of network isolation and Zero Trust-aligned architectures for AI pipelines, model services, orchestration layers, and connected cloud resources.
Detect and help mitigate model scraping, API abuse, excessive inference usage, and automated extraction attempts through control validation, monitoring inputs, and architectural recommendations.
Work with engineering and DevSecOps teams to embed security requirements into CI/CD pipelines, infrastructure-as-code, container pipelines, and deployment workflows.
Validate logging, monitoring, alerting, and audit controls to support detection, investigation, incident response, and compliance requirements in AI cloud environments.
Produce clear security assessment reports, findings, and prioritized remediation recommendations aligned with enterprise standards and risk expectations.
Support remediation discussions, provide implementation guidance, and track closure of identified security gaps.
Contribute reusable security patterns, reference architectures, architecture review checklists, and AI cloud security guidance assets to improve consistency and scale.
Professional & Technical Skills
Strong grasp of Generative and Agentic AI, prompt engineering, and AI evaluation frameworks. Ability to align AI capabilities with business objectives while ensuring scalability, responsible use, and tangible value realization. The candidate should be AI Native.
6 to 9 years of experience in cybersecurity, cloud security, application security, security engineering, or security architecture.
Strong hands-on experience securing workloads on AWS, Azure, and/or GCP.
Solid understanding of AI/ML and GenAI architectures, APIs, LLM-based applications, model hosting patterns, and data pipelines.
Experience with IAM, secrets management, encryption, network security, workload isolation, and cloud-native security controls.
Familiarity with CI/CD security, container security, Kubernetes, infrastructure-as-code, and deployment governance practices.
Practical understanding of AI-specific security risks including prompt injection, insecure tool access, data leakage, unsafe orchestration, model scraping, and misuse of inference endpoints.
Experience assessing APIs, gateways, microservices, and service-to-service trust boundaries in distributed cloud environments.
Good understanding of secure model hosting, GPU-backed workloads, and AI pipeline security requirements in enterprise cloud environments.
Familiarity with Zero Trust principles and network isolation strategies for sensitive workloads, APIs, and AI service integrations.
Strong analytical, documentation, and stakeholder communication skills, with the ability to translate technical findings into practical remediation guidance.
Preferred certifications include ISC2 CISSP (Certified Information Systems Security Professional), ISC2 CCSP (Certified Cloud Security Professional), ISACA CISM (Certified Information Security Manager), as well as advanced cloud security or architecture certifications across AWS, Azure, and GCP. Kubernetes-focused security credentials such as Certified Kubernetes Security Specialist (CKS) are also highly valued.
Additional Information
Location: Bengaluru, Hyderabad, Pune, Chennai, Mumbai, Gurugram (Gurgaon), Jaipur
15yrs education details AI Powered Tech Talent
Employment Type – Full Time.
15 years full time education
Project Role Description : Define the cloud security framework and architecture, ensuring it meets the business requirements and performance goals. Document the implementation of the cloud security controls and transition to cloud security-managed operations.
Must have skills : Secure AI
Good to have skills : NA
Minimum 5 year(s) of experience is required
Educational Qualification : 15 years full time education
Summary:
Seeking a forward-thinking professional with an AI-first mindset to design, develop, and deploy enterprise-grade solutions using Generative and Agentic AI frameworks that drive innovation, efficiency, and business transformation.
Hands-on and technically strong AI Cloud Security Engineer with 6+ years of experience and deep expertise in cloud security, application security, threat modeling, and security assessments for AI-enabled and cloud-native systems. This role is ideal for professionals who enjoy working closely with engineering, DevSecOps, architecture, and platform teams to identify design gaps, strengthen security controls, and improve resilience across modern AI/ML and GenAI ecosystems. You will assess, design, and enhance security controls for cloud platforms, LLM-based applications, model hosting environments, APIs, RAG pipelines, and agentic workflows, while helping secure deployments across AWS, Azure, and GCP. The ideal candidate demonstrates strong practitioner depth, sound security architecture understanding, and a practical awareness of AI-specific risks such as prompt injection, jailbreaks, insecure orchestration, data leakage, model scraping, API abuse, and over-privileged access in AI pipelines.
Roles & Responsibilities
Lead AI-driven solution design and delivery by applying GenAI and Agentic AI to address complex business challenges, automate processes, and integrate intelligent insights into enterprise workflows for measurable impact.
Lead security architecture reviews for AI-enabled and cloud-native platforms to identify design gaps, trust boundary issues, and control weaknesses across enterprise environments.
Perform end-to-end threat modeling and security risk assessments for AI, GenAI, and cloud environments, covering applications, APIs, data flows, infrastructure, orchestration services, and integrations.
Assess security risks across AI/ML and GenAI use cases, including LLM-based applications, inference services, RAG pipelines, and agentic workflows.
Review cloud deployments on AWS, Azure, and GCP to validate secure configuration of IAM, networking, storage, compute, containers, Kubernetes, serverless services, logging, and encryption.
Evaluate security of APIs, gateways, and microservices, including authentication, authorization, token handling, rate limiting, throttling, and input validation.
Assess AI-specific threats such as prompt injection, jailbreaks, over-privileged tool access, data leakage, model misuse, insecure orchestration patterns, and unsafe trust boundaries.
Review the security design of AI pipelines, including data ingestion, preprocessing, embeddings, vector stores, training, inference, and monitoring components.
Secure GPU-based AI workloads and model hosting environments by validating access controls, workload protection, isolation boundaries, and monitoring coverage.
Assess and guide implementation of network isolation and Zero Trust-aligned architectures for AI pipelines, model services, orchestration layers, and connected cloud resources.
Detect and help mitigate model scraping, API abuse, excessive inference usage, and automated extraction attempts through control validation, monitoring inputs, and architectural recommendations.
Work with engineering and DevSecOps teams to embed security requirements into CI/CD pipelines, infrastructure-as-code, container pipelines, and deployment workflows.
Validate logging, monitoring, alerting, and audit controls to support detection, investigation, incident response, and compliance requirements in AI cloud environments.
Produce clear security assessment reports, findings, and prioritized remediation recommendations aligned with enterprise standards and risk expectations.
Support remediation discussions, provide implementation guidance, and track closure of identified security gaps.
Contribute reusable security patterns, reference architectures, architecture review checklists, and AI cloud security guidance assets to improve consistency and scale.
Professional & Technical Skills
Strong grasp of Generative and Agentic AI, prompt engineering, and AI evaluation frameworks. Ability to align AI capabilities with business objectives while ensuring scalability, responsible use, and tangible value realization. The candidate should be AI Native.
6 to 9 years of experience in cybersecurity, cloud security, application security, security engineering, or security architecture.
Strong hands-on experience securing workloads on AWS, Azure, and/or GCP.
Solid understanding of AI/ML and GenAI architectures, APIs, LLM-based applications, model hosting patterns, and data pipelines.
Experience with IAM, secrets management, encryption, network security, workload isolation, and cloud-native security controls.
Familiarity with CI/CD security, container security, Kubernetes, infrastructure-as-code, and deployment governance practices.
Practical understanding of AI-specific security risks including prompt injection, insecure tool access, data leakage, unsafe orchestration, model scraping, and misuse of inference endpoints.
Experience assessing APIs, gateways, microservices, and service-to-service trust boundaries in distributed cloud environments.
Good understanding of secure model hosting, GPU-backed workloads, and AI pipeline security requirements in enterprise cloud environments.
Familiarity with Zero Trust principles and network isolation strategies for sensitive workloads, APIs, and AI service integrations.
Strong analytical, documentation, and stakeholder communication skills, with the ability to translate technical findings into practical remediation guidance.
Preferred certifications include ISC2 CISSP (Certified Information Systems Security Professional), ISC2 CCSP (Certified Cloud Security Professional), ISACA CISM (Certified Information Security Manager), as well as advanced cloud security or architecture certifications across AWS, Azure, and GCP. Kubernetes-focused security credentials such as Certified Kubernetes Security Specialist (CKS) are also highly valued.
Additional Information
Location: Bengaluru, Hyderabad, Pune, Chennai, Mumbai, Gurugram (Gurgaon), Jaipur
15yrs education details AI Powered Tech Talent
Employment Type – Full Time.
15 years full time education
About Accenture
Accenture is a leading global professional services company that helps the world’s leading businesses, governments and other organizations build their digital core, optimize their operations, accelerate revenue growth and enhance citizen services—creating tangible value at speed and scale. We are a talent- and innovation-led company with approximately 791,000 people serving clients in more than 120 countries. Technology is at the core of change today, and we are one of the world’s leaders in helping drive that change, with strong ecosystem relationships. We combine our strength in technology and leadership in cloud, data and AI with unmatched industry experience, functional expertise and global delivery capability. Our broad range of services, solutions and assets across Strategy & Consulting, Technology, Operations, Industry X and Song, together with our culture of shared success and commitment to creating 360° value, enable us to help our clients reinvent and build trusted, lasting relationships. We measure our success by the 360° value we create for our clients, each other, our shareholders, partners and communities.Visit us at www.accenture.com
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Security pay context
Based on 1,675 disclosed Security salaries on RoleSuite, the role pays a median of $142K/year, with most offers between $113K and $183K (10th–90th percentile: $91K–$216K).
See the full Security salary breakdown →