> Markdown version of [/jobs/ext/3984203-ai-security-architect](https://www.wearedevelopers.com/jobs/ext/3984203-ai-security-architect). 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 Architect

- **Company:** Resourcesoft, Inc.
- **Location:** Malvern, PA, United States
- **Experience:** Expert
- **Contract:** Contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Cloud Computing, Code Generation, Continuous Integration, Information Leak Prevention, Identity and Access Management, OpenAI, Microsoft Copilot, Azure Machine Learning, Security Information and Event Management, Data Logging, GitHub Copilot, Retrieval-Augmented Generation, Large Language Models, Software Security, Agentic-AI, AI Platforms, Prompt Injection, Claude, Artificial Intelligence Governance, Splunk, GPT, Devsecops
- **Published:** October 28, 2026
- **Apply:** https://www.dice.com/job-detail/b27e394a-65f9-4bd7-994b-e5151e82544e

## About the Role

* 5 years or more of experience with security engineering or architecture, including hands-on AI/ML platform security.
 * Proficiency in Azure AI Foundry, Microsoft Copilot, Claude, OpenAI/ChatGPT, GitHub Copilot, and native governance controls.
 * Experience with LLMs, agentic AI, RAG, APIs, prompt injection, data leakage, and model abuse risk mitigation.
 * Experience with SIEM, EDR, DLP, IAM, CASB, Microsoft Purview, Defender for Cloud Apps, and Splunk for AI use cases.
 * Experience in NIST AI RMF, FFIEC, DevSecOps, CI/CD security reviews, and regulated financial services environments.
 * Excellent verbal and written communication skills.

## Description

* Architect secure deployment patterns for AI platforms, agentic systems, AI agents, and AI-generated code.
 * Evaluate AI platform releases, feature updates, and configuration changes for security impact before adoption.
 * Configure AI guardrails, access controls, logging, monitoring, and data protection across the AI ecosystem.
 * Secure AI connectors, plugins, and integrations with least-privilege permissions and continuous usage monitoring.
 * Govern sensitive data and PII flows through AI tools using classification, labeling, and DLP practices.
 * Assess AI tools, models, and emerging threats while reporting KPIs, KRIs, findings, and remediation recommendations.