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.
AI Security Engineering Specialist professional with 6–9 years of experience in security engineering, DevSecOps, MLOps security, or AI/ML security, with strong hands-on expertise in implementing and operating security controls across machine learning and GenAI systems throughout their lifecycle. Focused on securing ML pipelines, platforms, and models in an execution-driven environment, ensuring they are compliant, scalable, and resilient against ML-specific and cloud-native threats.
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.
Implement security best practices across ML and GenAI pipelines, covering data ingestion, model training, deployment, inference, and monitoring.
Secure ML workflows against threats such as data poisoning, model poisoning, adversarial attacks, model theft, inference abuse, and unauthorized usage.
Perform threat modeling and security assessments for ML and GenAI architectures to identify risks across pipelines, endpoints, and trust boundaries.
Support security testing of GenAI systems, including prompt injection, jailbreak attempts, prompt leakage, unsafe output scenarios, and abuse vectors.
Implement and validate ML governance controls covering bias, data drift, model drift, explainability, lineage, auditability, and compliance requirements.
Configure and maintain RBAC, secrets management, encryption, and comprehensive audit logging for ML platforms and pipelines.
Implement runtime protections for AI systems, including inference time controls, guardrail enforcement, rate limiting, token protection, and abuse detection mechanisms.
Secure model endpoints against extraction, misuse, and unauthorized access through monitoring, throttling, authentication, and authorization controls.
Implement AI specific monitoring to detect misuse patterns, anomalous inference behavior, data leakage signals, and operational security issues.
Maintain ML software supply chain security, including dependency validation, dataset lineage, model provenance tracking, and ML BOM practices.
Build, operate, and integrate secure ML CI/CD pipelines using tools such as GitLab CI, Azure DevOps, or Jenkins, embedding security scanning and policy controls.
Deploy, operate, and secure containerized ML workloads using Docker and Kubernetes, including image scanning, runtime protection, and secure configuration.
Work with ML platforms such as Azure ML, AWS SageMaker, GCP Vertex AI, and Databricks to deploy and operate secure ML workloads in cloud environments.
Monitor ML systems for data drift, model drift, performance degradation, security anomalies, and operational issues in production environments.
Collaborate with data science, DevOps, platform, and security teams to embed security controls and best practices into end to end ML workflows.
Produce and maintain technical documentation, operational runbooks, and evidence artifacts to support audits, reviews, and ongoing secure operations.
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.
6–9 years of experience in ML, MLOps, DevSecOps, or security engineering.
Strong hands-on experience with Python for ML pipelines, automation, and security tooling.
Experience with ML frameworks such as TensorFlow, PyTorch, or Scikit Learn.
Practical knowledge of ML software supply chain security, ML security risks, adversarial ML attacks, and ML attack surfaces.
Experience with policy-as-code for MLOps or LLMOps.
Knowledge of differential privacy, model watermarking/fingerprinting, and secure model sharing techniques
Familiarity with confidential computing / trusted execution environments (TEE) for secure inference
Experience securing CI/CD pipelines and containerized workloads.
Familiarity with incident handling in ML platforms, including model rollback, pipeline containment, and forensic readiness
Experience implementing policy-as-code and automated governance enforcement in MLOps or LLMOps pipelines
Working knowledge of Kubernetes, Docker, and cloud platforms (AWS, Azure, or GCP).
Experience with monitoring and logging solutions such as ELK, Prometheus, or Grafana.
Familiarity with AI governance, Responsible AI practices, and regulatory expectations.
Preferred certifications: Certified AI Security Specialist (CAISS), CISSP, ISACA Artificial Intelligence Security Management (AAISM) and Certified Offensive AI Security Professional (COASP).
Additional Information:
6 to 9 years of relevant experience in the security design, governance, and operational hardening of enterprise scale ML and GenAI platforms
Employment Type: Full Time
Location: Bengaluru, Hyderabad, Pune, Chennai, Mumbai, Gurugram (Gurgaon), Jaipur
15 year full time education is required: AI Powered Tech Talent
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.
AI Security Engineering Specialist professional with 6–9 years of experience in security engineering, DevSecOps, MLOps security, or AI/ML security, with strong hands-on expertise in implementing and operating security controls across machine learning and GenAI systems throughout their lifecycle. Focused on securing ML pipelines, platforms, and models in an execution-driven environment, ensuring they are compliant, scalable, and resilient against ML-specific and cloud-native threats.
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.
Implement security best practices across ML and GenAI pipelines, covering data ingestion, model training, deployment, inference, and monitoring.
Secure ML workflows against threats such as data poisoning, model poisoning, adversarial attacks, model theft, inference abuse, and unauthorized usage.
Perform threat modeling and security assessments for ML and GenAI architectures to identify risks across pipelines, endpoints, and trust boundaries.
Support security testing of GenAI systems, including prompt injection, jailbreak attempts, prompt leakage, unsafe output scenarios, and abuse vectors.
Implement and validate ML governance controls covering bias, data drift, model drift, explainability, lineage, auditability, and compliance requirements.
Configure and maintain RBAC, secrets management, encryption, and comprehensive audit logging for ML platforms and pipelines.
Implement runtime protections for AI systems, including inference time controls, guardrail enforcement, rate limiting, token protection, and abuse detection mechanisms.
Secure model endpoints against extraction, misuse, and unauthorized access through monitoring, throttling, authentication, and authorization controls.
Implement AI specific monitoring to detect misuse patterns, anomalous inference behavior, data leakage signals, and operational security issues.
Maintain ML software supply chain security, including dependency validation, dataset lineage, model provenance tracking, and ML BOM practices.
Build, operate, and integrate secure ML CI/CD pipelines using tools such as GitLab CI, Azure DevOps, or Jenkins, embedding security scanning and policy controls.
Deploy, operate, and secure containerized ML workloads using Docker and Kubernetes, including image scanning, runtime protection, and secure configuration.
Work with ML platforms such as Azure ML, AWS SageMaker, GCP Vertex AI, and Databricks to deploy and operate secure ML workloads in cloud environments.
Monitor ML systems for data drift, model drift, performance degradation, security anomalies, and operational issues in production environments.
Collaborate with data science, DevOps, platform, and security teams to embed security controls and best practices into end to end ML workflows.
Produce and maintain technical documentation, operational runbooks, and evidence artifacts to support audits, reviews, and ongoing secure operations.
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.
6–9 years of experience in ML, MLOps, DevSecOps, or security engineering.
Strong hands-on experience with Python for ML pipelines, automation, and security tooling.
Experience with ML frameworks such as TensorFlow, PyTorch, or Scikit Learn.
Practical knowledge of ML software supply chain security, ML security risks, adversarial ML attacks, and ML attack surfaces.
Experience with policy-as-code for MLOps or LLMOps.
Knowledge of differential privacy, model watermarking/fingerprinting, and secure model sharing techniques
Familiarity with confidential computing / trusted execution environments (TEE) for secure inference
Experience securing CI/CD pipelines and containerized workloads.
Familiarity with incident handling in ML platforms, including model rollback, pipeline containment, and forensic readiness
Experience implementing policy-as-code and automated governance enforcement in MLOps or LLMOps pipelines
Working knowledge of Kubernetes, Docker, and cloud platforms (AWS, Azure, or GCP).
Experience with monitoring and logging solutions such as ELK, Prometheus, or Grafana.
Familiarity with AI governance, Responsible AI practices, and regulatory expectations.
Preferred certifications: Certified AI Security Specialist (CAISS), CISSP, ISACA Artificial Intelligence Security Management (AAISM) and Certified Offensive AI Security Professional (COASP).
Additional Information:
6 to 9 years of relevant experience in the security design, governance, and operational hardening of enterprise scale ML and GenAI platforms
Employment Type: Full Time
Location: Bengaluru, Hyderabad, Pune, Chennai, Mumbai, Gurugram (Gurgaon), Jaipur
15 year full time education is required: AI Powered Tech Talent
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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We believe that no one should be discriminated against because of their differences. All employment decisions shall be made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, military veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other basis as protected by applicable law. Our rich diversity makes us more innovative, more competitive, and more creative, which helps us better serve our clients and our communities.
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 →