> Markdown version of [/jobs/ext/4543836-ai-security-engineer](https://www.wearedevelopers.com/jobs/ext/4543836-ai-security-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# AI Security Engineer

- **Company:** TEKSYSTEMS INC.
- **Location:** Charlotte, NC, United States
- **Salary:** $135,200.0 - $149,760.0
- **Contract:** Temporary contract
- **Skills:** Artificial Intelligence, Application Integration Architecture, Microsoft Azure, Information Leak Prevention, Monitoring of Systems, Python (Programming Language), Node.Js, Open Web Application Security, OpenAI, Cloud Services, Microsoft Copilot, Security Information and Event Management, Systems Integration, Data Logging, Scripting, Retrieval-Augmented Generation, Large Language Models, Generative AI, Agentic-AI, Rate Limiting, Web Filtering, Build Management, AI Platforms, Microsoft Sentinel, Azure AI, Api Gateway, Restful APIs, Splunk
- **Published:** November 3, 2026
- **Apply:** https://dejobs.org/x/x/BA82B2515733413A9AFCDD1E57F1295F/job/

## About the Role

* Strong hands-on experience with Generative AI, LLMs, or AI engineering.
 * Experience building and deploying production AI/LLM solutions, rather than solely performing AI governance or risk assessments.
 * Experience implementing AI/LLM security controls and guardrails.
 * Hands-on experience with Microsoft AI Foundry, Azure AI, Azure OpenAI, or closely related Microsoft AI technologies.
 * Strong scripting/development experience with Python.
 * Experience integrating applications with SIEM/security monitoring platforms.
 * Understanding of common AI security risks, including:
 * Prompt injection
 * Sensitive information disclosure
 * Data leakage
 * Insecure output handling
 * Excessive agent permissions/autonomy
 * RAG and vector/embedding vulnerabilities
 * Model and AI supply-chain risk
 * Experience working with REST APIs and integrating security controls into applications and cloud services.

Preferred / Nice-to-Have Skills

 * Experience with agentic AI architectures and AI agents.
 * Experience with Microsoft Sentinel, Splunk, or other enterprise SIEM platforms.
 * Experience securing RAG applications and vector databases.
 * Familiarity with OWASP guidance for LLM/Generative AI security.
 * Experience with AI gateways, API gateways, content filtering, DLP, or PII detection.
 * Development experience with Node.js or Go.
 * Previous experience implementing AI security controls in a highly regulated environment such as financial services.

## Description

We are seeking a hands-on AI Security Engineer to help design, build, and secure enterprise Generative AI solutions. This individual will work closely with engineering and security teams to implement security controls across LLM-powered applications, copilots, RAG solutions, and agentic AI systems. This is not a governance-only or advisory position. The ideal candidate will have experience building and deploying AI solutions, developing security guardrails, writing Python code, and integrating AI applications with enterprise security platforms., * Design and implement security controls for LLM-powered applications, Generative AI platforms, copilots, RAG solutions, and agentic AI systems.

 * Build and deploy AI security guardrails to protect against prompt injection, sensitive data exposure, data leakage, insecure model outputs, and excessive agent autonomy.
 * Develop security integrations within the Microsoft AI Foundry / Azure AI ecosystem.
 * Design and implement AI gateway controls to centralize security policy enforcement across model traffic.
 * Implement capabilities such as:
 * Prompt inspection and filtering
 * LLM response filtering
 * PII and sensitive-data redaction
 * Secrets detection
 * Model access controls
 * Rate limiting and abuse prevention
 * Security logging and audit trails
 * Integrate AI applications and agentic workflows with SIEM platforms such as Microsoft Sentinel, Splunk, or similar security monitoring technologies.
 * Develop automation, security tooling, and AI integrations using Python.
 * Address security risks related to RAG, vector databases, embeddings, model supply chains, third-party models, and AI agents.
 * Partner with software, cloud, AI, and cybersecurity teams to incorporate secure-by-design principles throughout AI application development and deployment.
 * Help protect AI applications processing customer, employee, and regulated financial data.