Hands-on cybersecurity engineering experience in application security, cloud security, infrastructure/network security, DevSecOps/CI-CD security, vulnerability management, and security architecture/zero trust.
Experience designing and implementing security controls for cloud-native applications in Azure and/or AWS.
Strong understanding of secure SDLC practices, including code security, pipeline security, secrets handling, dependency risk, and deployment hardening.
Relevant certifications such as Security+, CISSP, CCSP, GIAC, or cloud security certifications.
Requirements:
Design, build, secure, and deploy Agentic AI use cases that improve cybersecurity operations and enterprise workflows.
Integrate security into the full lifecycle of AI-enabled solutions, including requirements definition, architecture review, secure implementation, testing, deployment, and operational monitoring.
Perform threat modeling and integrate security controls into CI/CD and DevSecOps pipelines.
Define and apply AI guardrails, monitoring and logging requirements, and policy enforcement mechanisms to support responsible AI usage.
Job description
Our client, a Commercial Banking company, is looking for a Senior Security Engineer – Agentic AI for their Remote location.
Responsibilities:
The Cyber Security Engineer will design, build, secure, and deploy Agentic AI use cases that improve cybersecurity operations, enterprise workflows, and security-adjacent automation.
This role is hands-on and engineering-focused, with responsibility for integrating security into the full lifecycle of AI-enabled solutions, including requirements definition, architecture review, secure implementation, testing, deployment, and operational monitoring.
The engineer will work across cloud-native and enterprise environments to help create AI systems that can safely use tools, interact with APIs, automate workflows, and operate with appropriate guardrails, human oversight, and access controls.
The role will contribute to the development of security copilots, incident-response orchestration capabilities, enterprise workflow agents, and knowledge-driven assistants that may interact with sensitive systems or data.
In addition to building secure Agentic AI capabilities, this role will apply core cybersecurity engineering disciplines to reduce risk across the environment.
Responsibilities include performing threat modeling, integrating security controls into CI/CD and DevSecOps pipelines, supporting vulnerability management and remediation, implementing identity and access controls for AI services and connected tools, and ensuring secure cloud deployment practices across Azure and AWS environments.
The engineer will help define and apply AI guardrails, prompt and context protections, monitoring and logging requirements, and policy enforcement mechanisms to support responsible AI usage, privacy expectations, and model-risk-aware deployment.
This position is collaborative but not leadership-oriented; success requires strong execution, sound engineering judgment, and the ability to partner effectively with application teams, platform teams, security architects, and governance stakeholders to deliver secure, scalable, and operationally viable Agentic AI solutions.
Requirements:
Hands-on cybersecurity engineering experience in any of the following areas application security, cloud security, infrastructure/network security, DevSecOps/CI-CD security, vulnerability management, and security architecture/zero trust.
Experience designing and implementing security controls for cloud-native applications in Azure and/or AWS.
Strong understanding of secure SDLC practices, including code security, pipeline security, secrets handling, dependency risk, and deployment hardening.
Experience securing systems that use external data sources, enterprise APIs, plugins, or automation tools
Ability to define and implement logging, monitoring, and observability requirements for AI-enabled applications and connected workflows.
Understanding of data protection, privacy, and sensitive data handling. Bonus able to demonstrate understanding of these principals as applied to AI workflows.
Strong troubleshooting and engineering execution skills with the ability to move from design to deployment.
Ability to communicate clearly with engineering, security, and governance stakeholders in a cross-functional environment.
Experience building or securing security AI systems, threat-hunting assistants, enterprise workflow agents, or incident-response orchestration solutions
Exposure to retrieval-based systems, knowledge-grounded assistants, or secure data access patterns for AI applications
Experience with LLM application patterns, including orchestration frameworks, tool-calling patterns, and secure prompt/context management
Familiarity with AI red teaming, adversarial testing, model abuse cases, or attack-path analysis specific to generative AI systems
Familiarity with building or securing Agentic AI or AI-enabled workflows, especially around:
Prompt / context security
Guardrails / policy enforcement
Human-in-the-loop workflows
Experience with policy-as-code, infrastructure-as-code security controls, or automated governance checks.
Knowledge of enterprise security and compliance frameworks relevant to AI and modern cloud environments
Experience integrating AI systems with SIEM, SOAR, case management, ticketing systems, or security operations workflows
Understanding of Azure OpenAI, Azure security controls, AWS AI/security services, or related enterprise AI platform services
Relevant certifications such as Security+, CISSP, CCSP, GIAC, or cloud security certifications
Experience working in environments with higher assurance or regulated security expectations
