Security / Compliance Engineering

Kasmo Inc

Jersey City, NJ

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JOB DETAILS

SKILLS

Amazon Web Services (AWS), Application Programming Interface (API), Artificial Intelligence (AI), Blueprints, Business Case, Cloud Computing, Computer Security, Continuous Deployment/Delivery, Continuous Integration, Cryptography, Data Modeling, Data Recovery, DevOps, Document Management, Documentation, Incident Response, Information/Data Security (InfoSec), Injections, Internet Security, Loss Prevention, Open Source, Penetration Testing, Privacy Controls, Privacy Regulations, Risk, Risk Management, Risk Modeling, Security Infrastructure, Software Development Lifecycle (SDLC), Support Documentation, Team Player, Testing, Threat Modeling, Use Cases

LOCATION

Jersey City, NJ

POSTED

4 days ago

Description:
This role requires working onsite 4 days per week, and a F2F interview at the client s Jersey City location is mandatory.
Only USCs/GCs are eligible
AI Security & Compliance Engineer
AI/ML Security / GenAI Risk / Cloud Security / DevSecOps
Level
Security Specialist / Senior Engineer
Target / alternate titles
AI Security Engineer; ML Security Engineer; GenAI Security Engineer; Cloud Security Engineer - AI; DevSecOps Engineer - AI; AI Compliance Engineer
Core keywords
AI security, LLM security, prompt injection, AI red teaming, adversarial ML, AWS security, IAM, KMS, secrets, DevSecOps, Terraform, IaC, CI/CD, Kubernetes, data leakage, model supply chain, secure RAG, privacy, compliance
Recruiter red flags
Traditional security profile with no AI/ML awareness; cannot explain prompt injection or RAG data leakage; no AWS security depth; no Terraform/IaC or pipeline control experience; overly theoretical without implementation evidence.
Role purpose
Ensure AI and GenAI systems on AIRP are designed, deployed, and operated securely and in compliance with enterprise technology, cybersecurity, privacy, and regulatory standards. The role covers emerging LLM risks as well as traditional AWS cloud, application, data-security, DevSecOps, and IaC controls.
Client-specific emphasis
Security must cover AWS-hosted AIRP, Terraform/IaC templates, CI/CD pipelines, cloud engineering patterns, AI applications, and model/data access controls.
The role must help create reusable controls that work across multiple business AI use cases and can support the broader cloud-agnostic blueprint.
Power Platform / Copilot Studio governance, data-loss prevention, connector controls, and citizen-development oversight are valuable plus areas.
Primary ownership
Security architecture and control implementation for AI platforms, LLM applications, RAG pipelines, model-serving environments, and agentic systems.
Threat modeling, AI red teaming, vulnerability assessment, risk remediation, and secure production approvals.
Security evidence, control documentation, and compliance support for AIRP releases, Terraform/IaC, and DevOps pipelines.
Key responsibilities
Design and review secure architectures for AI/ML platforms, LLM applications, RAG pipelines, model-serving environments, and agentic AI workflows.
Conduct threat modeling for prompt injection, jailbreaks, insecure tool use, model inversion, data leakage, retrieval poisoning, adversarial inputs, unauthorized access, and third-party model risk.
Implement controls for AWS IAM, encryption, key management, secrets management, network segmentation, API security, logging, secure data handling, and data-loss prevention.
Embed security into MLOps, LLMOps, CI/CD, container security, infrastructure-as-code, Terraform modules, and deployment pipelines.
Review cloud-agnostic IaC templates and AWS-specific deployments for least privilege, secure defaults, segregation of duties, policy compliance, and auditability.
Review third-party models, APIs, open-source packages, AI tools, and vendor platforms for security, privacy, model supply-chain, and compliance risks.
Build monitoring and alerting for suspicious AI usage, anomalous access, policy violations, unsafe interactions, and potential data leakage.
Support AI red teaming, penetration testing, vulnerability management, incident response, remediation planning, and production-readiness reviews.
Maintain audit-ready documentation for controls, testing, risk acceptance, remediation, and production approvals.
Must-have candidate profile
Strong background in cybersecurity, cloud security, application security, DevSecOps, or technology risk.
Experience securing cloud-native platforms, APIs, microservices, containers, Kubernetes, CI/CD pipelines, and infrastructure-as-code.
Strong AWS cloud security exposure or comparable hyperscaler security depth, including IAM, encryption, network controls, logging, secrets, and secure deployment patterns.
Understanding of AI/ML and GenAI-specific risks such as prompt injection, adversarial attacks, data leakage, model misuse, retrieval poisoning, model supply-chain risk, and unsafe tool use.
Familiarity with threat modeling, vulnerability management, security testing, incident response, secure SDLC, DevSecOps, and Terraform/IaC controls.
Ability to work directly with engineering teams to implement practical, risk-based controls.
Preferred experience
Experience securing AI/ML platforms or GenAI applications in production.
Financial-services security, technology risk, regulatory, compliance, privacy, or audit experience.
Familiarity with AI red teaming, secure RAG design, LLM gateways, Power Platform governance, Copilot Studio controls, data-loss prevention, and privacy-by-design controls.
Initial screening questions
How would you threat-model a RAG system that accesses confidential enterprise documents?
What controls reduce prompt injection and data leakage risks?
How would you secure an AWS-hosted AIRP deployment using IAM, encryption, logging, and network controls?
How do you embed security into Terraform modules and CI/CD pipelines?
How do you secure an AI model-serving pipeline from source to production?
What AI-specific red-team tests have you designed or executed?

About the Company

Kasmo Inc

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