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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Security Engineer - **Company:** Fisher & Phillips LLP - **Location:** Sandy Springs, GA, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Microsoft Access, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Software as a Service, Cloud Computing, Cloud Computing Security, Cyber Security, Computer Programming, Continuous Integration, Information Leak Prevention, Data Security, Fuzz Testing, Identity and Access Management, Python (Programming Language), Key Management, Machine Learning, Open Web Application Security, Windows PowerShell, Systems Development Life Cycle, OpenAI, Cloud Services, Azure Machine Learning, Secure Coding, Security Information and Event Management, Data Streaming, Software Vulnerability Management, Policy as Code, Data Logging, Scripting, Data Classification, Retrieval-Augmented Generation, Large Language Models, Google Vertex AI, Software Security, Llamaindex, Generative AI, Agentic-AI, Web Filtering, AI Platforms, Information Technology, Api Management - **Published:** November 1, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=5232ae6b20c43160 ## About the Role * Bachelor's degree in Cybersecurity, Computer Science, Information Technology, Engineering, or a related field (or equivalent experience). * 5+ years of security engineering, application/product security, cloud security, software security, or related cybersecurity experience. * Hands-on experience securing generative AI and LLM systems, including RAG, agents, model/API integrations, and enterprise AI platforms. * Experience with threat modeling, secure design/SDLC, application/API security testing, IAM, secrets management, and security logging/monitoring. * Proficiency in Python or another programming/scripting language; experience with APIs, automation, CI/CD, or security tooling. * Knowledge of AI threats and mitigations, including prompt injection, data leakage/poisoning, model extraction, insecure tool use, excessive agency, and AI supply-chain risk; familiarity with cloud AI services., * Experience with AI red teaming, adversarial testing, abuse-case analysis, or security evaluations of LLM, RAG, and agentic systems. * Experience with vector databases, model gateways, LangChain/LlamaIndex, MCP, and major cloud AI services such as Azure OpenAI, AWS Bedrock, or Google Vertex AI. * Familiarity with the OWASP Top 10 for LLM Applications, MITRE ATLAS, and NIST AI RMF; experience with IaC, policy-as-code, containers, or CI/CD security is a plus. * Relevant security, cloud, or AI certifications; experience in a law firm, professional services, regulated industry, or client-service environment is preferred. Key Competences * Strong hands-on security engineering, coding, and analytical problem-solving skills * Strong threat-modeling and adversarial thinking for AI, LLM, RAG, and agentic systems * Ability to translate AI use cases and emerging risks into secure, practical technical controls * Strong automation skills using scripting, APIs, and modern engineering workflows * Clear communication with technical and non-technical stakeholders * Sound judgment, intellectual curiosity, and a continuous-learning mindset ## Description The AI Security Engineer will serve as a hands-on security specialist and trusted technical partner to cybersecurity, IT, data, application, privacy, compliance, and business teams. The ideal candidate combines security engineering experience with practical knowledge of AI/ML technologies, cloud security, secure development, threat modeling, and risk-based decision-making., AI Security Architecture & Strategy * Design and maintain security architecture for generative AI, machine learning, AI-enabled SaaS applications, agents, APIs, and supporting cloud services. * Establish security standards, reference patterns, and guardrails for AI development, deployment, integration, and use. * Perform security reviews and threat modeling for AI solutions, including model access, data flows, retrieval-augmented generation, plugins, tools, agents, and third-party integrations. * Evaluate AI technologies, models, vendors, and services to ensure security and privacy requirements are incorporated into design and implementation. AI/ML Platform & Application Security * Implement controls to protect AI platforms, model endpoints, APIs, orchestration layers, vector databases, development environments, and supporting cloud infrastructure. * Assess and reduce risks such as prompt injection, sensitive data exposure, insecure tool use, excessive agency, model supply-chain compromise, and unauthorized AI usage. * Partner with application and platform teams to embed secure development, testing, secrets management, logging, and vulnerability remediation into AI solutions. Data, Model & Identity Protection * Apply least privilege, strong authentication, service identity, privileged access, and lifecycle controls to AI platforms, models, agents, and workloads. * Protect training, grounding, prompt, output, and telemetry data using data classification, encryption, DLP, access controls, and appropriate retention practices. * Review model and agent permissions, data access, connectors, and tool integrations to identify excessive privileges, unsafe trust relationships, and unintended data exposure. AI Security Engineering & Monitoring * Implement and optimize security controls for AI systems, including model gateways, content filtering, runtime guardrails, API security, cloud security, SIEM, DLP, and monitoring. * Develop detections and response procedures for AI-related misuse, policy violations, data leakage, anomalous model activity, and compromised integrations. * Automate security validation, testing, monitoring, and enforcement using Python, PowerShell, APIs, CI/CD, infrastructure-as-code, or orchestration tools. Governance, Risk & Collaboration * Partner with compliance, privacy, legal, data, and business stakeholders to align AI security controls with policies, client expectations, governance requirements, and organizational risk objectives. * Develop documentation for AI security standards, architecture decisions, threat models, control designs, risk exceptions, and remediation plans. * Support AI risk assessments, incident investigations, tabletop exercises, and reviews of emerging AI threats, vulnerabilities, and attack techniques. * Communicate AI security risks and recommendations clearly to technical and non-technical stakeholders.