AI Security Architect

SMART TECH SKILLS LLC•Sinking Spring, PA

1d•Onsite

About The Position

The AI Security Architect secures AI platforms, models, agentic systems, and AI-enabled integrations in a regulated financial services environment. The role focuses on secure AI architecture, governance controls, data protection, threat mitigation, and continuous monitoring across the AI ecosystem. It evaluates platform changes, establishes security guardrails, and addresses risks involving LLMs, RAG, APIs, prompt injection, data leakage, and model abuse, collaborating across security and delivery functions. The position is onsite in Malvern, Pennsylvania, and is listed as mid–senior level with 5+ years of relevant experience.

Requirements

5+ years 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 with NIST AI RMF, FFIEC, DevSecOps, CI/CD security reviews, and regulated financial services environments.
Strong security architecture and risk assessment skills.
Ability to evaluate rapidly evolving AI technologies and emerging security threats.
Strong analytical skills for identifying security findings and developing remediation recommendations.
Ability to collaborate effectively across security and delivery functions.
Detail-oriented approach to governance, monitoring, and data protection.
Excellent verbal and written communication skills.

Nice To Haves

Experience assessing emerging AI threats and security risks across AI tools and models.
Experience reporting security KPIs, KRIs, findings, and remediation recommendations.

Responsibilities

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 native governance controls and security guardrails across AI platforms.
Implement access controls, logging, monitoring, and data protection across the AI ecosystem.
Secure AI connectors, plugins, APIs, and integrations using least-privilege permissions.
Continuously monitor AI usage to identify security and access risks.
Govern sensitive data and PII flows through AI tools using classification, labeling, and DLP practices.
Mitigate risks involving prompt injection, data leakage, model abuse, LLMs, agentic AI, and RAG implementations.
Apply SIEM, EDR, DLP, IAM, CASB, Microsoft Purview, Defender for Cloud Apps, and Splunk capabilities to AI use cases.
Assess AI tools, models, and emerging threats for security risks.
Perform security reviews supporting DevSecOps and CI/CD practices.
Report KPIs, KRIs, security findings, and remediation recommendations.