1.1 Overview DNEG is seeking a Security Engineer to help teams adopt AI and other technologies safely across business and production workflows. Reporting to the Principal Security Architect, the role combines application and cloud security with hands-on development, integration, and testing of practical security solutions. The engineer will translate agreed architecture and security requirements into working controls, support secure pilots and production readiness, and provide evidence for informed approval decisions. Protecting client material, unreleased content, production assets, and proprietary workflows is central to the role. 1.2 Mandatory Requirements and Expectations An experienced individual is required who can: ● Own defined security reviews and engineering tasks from assessment through implementation, testing,remediation, and handover. Escalate material risks and significant design decisions to the Principal Security Architect; risk acceptance and release approvals remain with the designated approvers. ● Provide clear, evidence-based recommendations that account for security risk, business value, implementation effort, and operational support. Help teams find practical ways to deliver useful capabilities safely. ● Work closely with engineering, cloud, IT, production technology, and business teams to understand use cases and data flows, agree on controls, and integrate them into existing workflows. ● Maintain accurate design notes, review records, test evidence, approval conditions, and follow-up actions.
Define delivery milestones and measurable acceptance criteria with the Principal Security Architect and service owners. ● Explain findings and implementation choices clearly to technical and non-technical stakeholders. Provide practical guidance and demonstrations so teams can use approved capabilities confidently. 2 Duties and Operational Responsibilities ● Review and help secure application and API designs, including authentication, authorization, service identities, secrets, data protection, and secure integration. Write scripts and integration code to automate controls and reduce repeated manual work. ● Perform threat modelling for new systems and significant changes, including AI workflows. Identify how data or tools could be misused, implement agreed mitigations with delivery teams, and verify that the controls address the identified risks. ● Implement and validate cloud security controls for identity, network exposure, secrets, encryption, logging, and workloads. Work with platform owners on deployment automation, configuration management, and secure development pipelines. ● Build, integrate, and secure AI-enabled applications and supporting controls,



including AI gateways, approved model access, retrieval-augmented generation (RAG), and AI agents. Apply least-privilege access to tools and data, enforce user and service permissions, and require human approval for sensitive actions where appropriate. ● Assess AI providers and third-party tools for data flows, permissions, dependencies, retention, use of data for model training, and assurance evidence. Work with the relevant owners to apply company and client requirements to prompts, outputs, uploaded files, retrieved content, logs, and caches.● Plan and deliver working proof of concepts and controlled pilots using agreed success criteria. Test unauthorized access, prompt injection, sensitive-data disclosure, unsafe tool use, and control failures. Record limitations and evaluate performance, cost, reliability, and integration effort before recommending wider use. ● Prepare risk assessments and production-readiness recommendations supported by test evidence. Identify remediation, approval conditions, operating owners, monitoring needs, rollback arrangements, and unresolved risks before release. ● Track findings with system and service owners, validate fixes, and add repeatable checks to detect regressions when models, prompts, tools, permissions, or integrations change. Help investigate failures and security incidents affecting the solutions the role delivers. ● Produce reusable integration patterns, security checks, configuration guidance, and support runbooks. Deliver documented handovers to operating teams and report progress through working integrations, validated controls, resolved findings, and readiness against agreed acceptance criteria. 3 Job Requirements 3.1 Mandatory Job Requirements A successful candidate should demonstrate the following experience and capabilities: Approximately five years of relevant experience in application security, cloud security, security engineering, or security architecture, supported by demonstrable hands-on delivery and sound security fundamentals. Experience reviewing application designs and implementing secure development practices, threat modelling, least
- privilege access, and risk-based remediation in collaboration with engineering teams. Practical programming or scripting ability, such as Python, with experience integrating APIs,



handling authentication and secrets securely, debugging integrations, using version control, and writing repeatable tests. Working knowledge of code, dependency, and energetic security testing. Hands-on experience implementing security controls in at least one major cloud platform, particularly identity,networking, data protection, logging, and monitoring, with an understanding of deployment automation and operational support. Demonstrated experience building or integrating AI-enabled applications or workflows, such as model API integrations, retrieval systems, or agents that use tools. Ability to explain implementation choices, test results,limitations, and security controls through a practical example or demonstration. Experience assessing third-party products and services and tracing how company or client data is accessed, processed, stored, retained, and shared. Ability to translate data-handling requirements into practical technical controls. Practical understanding of AI security risks, including prompt injection, sensitive-data disclosure, excessive agent permissions, and unsafe use of model outputs. Ability to test and improve controls rather than relying solely on provider claims or model instructions. Ability to evaluate unfamiliar technologies through working proof of concepts, define measurable acceptance criteria, document evidence, and troubleshoot failures. Strong written communication, collaboration, and willingness to learn are essential. 3.2 Desired Job Requirements Additional experience in the following areas is desirable; candidates are not expected to have worked with every technology listed: ● Experience with AI gateways, agent frameworks, retrieval systems, or model evaluation, including secure deployment and integration into business or production workflows. ● Experience with containers, Kubernetes, infrastructure as code, and CI/CD security checks; familiarity with application,dependency, and cloud security assessment tools. ● Experience designing useful security logs, monitoring, incident-response procedures, and SIEM integration while limiting unnecessary collection of sensitive content; familiarity with relevant security standards and reference architectures. ● Experience supporting media and entertainment platforms, protecting high-value intellectual property, and working within client confidentiality and production requirements. 3.3 Education ● A bachelor's degree in computer science, information technology, cybersecurity, or equivalent practical experience is desirable. ● Relevant security or cloud certifications are desirable but are not a substitute for demonstrated practical experience.

📌 Security Engineer (Bengaluru)
🏢 DNEG
📍 Bengaluru