What are the responsibilities and job description for the AI Security & Compliance Engineer (AI/ML Security | GenAI Risk | Cloud Security | DevSecOps) position at Niche IT Software Solutions LLC?
AI Security & Compliance Engineer
AI/ML Security | GenAI Risk | Cloud Security | DevSecOps
Level: Security Specialist / Senior Engineer
Role Overview
We are seeking an AI Security & Compliance Engineer to secure enterprise AI platforms, LLM applications, RAG pipelines and agentic workflows. The role covers GenAI-specific risks, AWS cloud security, DevSecOps, privacy and regulatory compliance.
Key Responsibilities
- Design and review secure architectures for AI platforms, LLM applications, RAG pipelines and model-serving environments.
- Conduct threat modelling and AI red teaming for prompt injection, jailbreaks, data leakage, retrieval poisoning, adversarial attacks and unsafe tool usage.
- Implement AWS security controls covering IAM, encryption, KMS, secrets, networking, APIs, logging and data-loss prevention.
- Embed security into MLOps/LLMOps, Terraform/IaC, Kubernetes, containers and CI/CD pipelines.
- Assess third-party models, APIs, open-source packages and vendors for security, privacy and supply-chain risks.
- Monitor suspicious AI activity, unauthorised access, policy violations and potential data exposure.
- Support vulnerability management, penetration testing, incident response and production-readiness reviews.
- Maintain audit-ready security, compliance, testing and risk-remediation documentation.
Required Skills
- Strong background in cybersecurity, cloud security, application security, DevSecOps or technology risk.
- Experience securing APIs, microservices, containers, Kubernetes, CI/CD pipelines and infrastructure-as-code.
- Strong AWS security knowledge, including IAM, KMS, encryption, networking, secrets management and logging.
- Understanding of LLM and GenAI risks, including prompt injection, adversarial attacks, data leakage, retrieval poisoning and model supply-chain risks.
- Experience with threat modelling, secure SDLC, vulnerability management, incident response and Terraform/IaC controls.
- Ability to implement practical security controls with engineering teams.
Preferred Experience
- Production security experience with AI/ML platforms or GenAI applications.
- Financial services, regulatory compliance, privacy, audit or technology-risk experience.
- Familiarity with AI red teaming, secure RAG, LLM gateways, Power Platform/Copilot Studio governance and data-loss prevention.
Alternate Titles: AI Security Engineer, ML Security Engineer, GenAI Security Engineer, Cloud Security Engineer – AI, DevSecOps Engineer – AI or AI Compliance Engineer.