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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Cybersecurity with Ai or Cloud Security Engineer - **Company:** MUFG - **Location:** Jersey City, NJ, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Software System Penetration Testing, Cloud Computing Security, Cyber Security, Continuous Integration, Information Leak Prevention, DevOps, Identity and Access Management, Machine Learning, Open Web Application Security, Systems Development Life Cycle, Azure Machine Learning, Software Vulnerability Management, Data Logging, Microsoft Power Automate, Spring Cloud, Large Language Models, Software Security, Generative AI, Infrastructure as Code (IaC), Amazon Virtual Private Cloud (VPC), AI Platforms, Kubernetes, Terraform, Devsecops, Software Library, Docker, Vulnerability Analysis, Microservices - **Published:** July 17, 2026 - **Apply:** https://www.dice.com/job-detail/f5ffb5b6-19f0-4d3f-b143-b6335de3d9e5 ## About the Role · 8+ years of experience in Cybersecurity, Cloud Security, Application Security, or DevSecOps. · Strong hands-on experience with AWS Security (IAM, KMS, VPC, CloudTrail, GuardDuty, Secrets Manager, Encryption). · Experience securing Kubernetes, Docker, Microservices, APIs, and cloud-native applications. · Strong experience with Terraform and Infrastructure as Code (IaC). · Experience implementing security in CI/CD pipelines and DevSecOps environments. · Strong AWS cloud security exposure or comparable hyperscaler security depth, including IAM, encryption, network controls, logging, secrets, and secure deployment patterns. · Knowledge of AI/ML and Generative AI security concepts including: o Prompt Injection o AI Red Teaming o Adversarial Machine Learning o Secure RAG o Model Supply Chain Security o Data Leakage Prevention o Model Misuse o Retrieval Poisoning · Experience with vulnerability management, threat modeling, secure SDLC, incident response, and security testing. · Excellent understanding of cloud security architecture and enterprise security best practices. · Strong communication and stakeholder management skills. Preferred Qualifications · Experience securing enterprise AI/ML or Generative AI platforms in production. · Financial Services or Banking domain experience. · Experience with AI Governance, Privacy, Compliance, and Regulatory standards. · Knowledge of Power Platform Security, Microsoft Copilot Studio Governance, and Data Loss Prevention (DLP). · Familiarity with AI security frameworks such as OWASP Top 10 for LLMs, NIST AI RMF, and MITRE ATLAS. ## Description MUFG Union Bank is looking for an experienced AI/ML Security Engineer to join its AI Platform team in Jersey City, NJ. This role is responsible for securing enterprise AI/ML and Generative AI platforms by implementing robust cloud security, DevSecOps, AI risk management, and compliance controls. The ideal candidate will have strong expertise in AWS security, Infrastructure as Code (Terraform), Kubernetes, CI/CD security, and AI-specific security threats such as prompt injection, adversarial attacks, secure RAG, and model supply chain risks., · Design and implement security architecture for AI/ML platforms, LLM applications, RAG pipelines, and agentic AI systems. · Perform threat modeling for AI-specific risks including prompt injection, jailbreak attacks, adversarial ML, retrieval poisoning, model inversion, and data leakage. · Secure AWS cloud infrastructure using IAM, KMS, Secrets Manager, VPC security, encryption, logging, and monitoring. · Integrate security controls into DevSecOps pipelines, CI/CD workflows, Kubernetes, Docker, and Terraform-based Infrastructure as Code. · Review AI models, APIs, open-source libraries, and third-party AI services for security, privacy, and compliance risks. · Implement secure access controls, least privilege policies, API security, and data protection mechanisms. · Conduct vulnerability assessments, AI red teaming, penetration testing, and production security reviews. · Build monitoring, alerting, and logging for AI applications to detect anomalous behavior, policy violations, and data leakage. · Collaborate with AI engineers, cloud architects, and DevOps teams to implement enterprise security controls. · Maintain security documentation, audit evidence, compliance reports, and risk assessments. ## Related Videos - [The New AI Security Stack: Observe, Detect, Protect](https://www.wearedevelopers.com/videos/100302-the-new-ai-security-stack-observe-detect-protect) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [DevSecOps: Injecting Security into Mobile CI/CD Pipelines](https://www.wearedevelopers.com/videos/273-devsecops-injecting-security-into-mobile-ci-cd-pipelines) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) - [DevSecOps: Security in DevOps](https://www.wearedevelopers.com/videos/36-devsecops-security-in-devops) ## Related Articles - [Got AI ideas but no money? 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