AI Security Engineer

Job not on LinkedIn

🔥 4 minutes ago

🇺🇸 United States – Remote

⏰ Full Time

🟠 Senior

🔴 Lead

👮‍♂️ Cybersecurity / Security Engineer

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LTS

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

🏥 Healthcare

💼 Consulting

📦 Logistics

Healthcare • Consulting • Logistics

LTS is a government-focused mission support and healthcare services firm that provides emergency response, occupational health, automated healthcare solutions, and enterprise consulting. It offers rapid nationwide deployment for disaster response, logistics and mass care, and delivers automated diagnostic kiosks, diagnostic lab testing, medical staffing, on-site clinics, and testing/vaccination management for underserved and remote populations. LTS also provides IT modernization, cloud and systems integration, cybersecurity and risk management, AI/ML and data analytics, and program management for federal, state, and local clients with strong compliance (HIPAA and federal standards) and public-sector acquisition experience.

📋 Description

• Design and implement security controls for autonomous and multi-agent AI systems. • Secure agent orchestration, tool execution, memory, and external integrations. • Identify and mitigate emerging AI-specific attack vectors and vulnerabilities. • Secure Retrieval-Augmented Generation (RAG) pipelines, embeddings, vector databases, and enterprise knowledge repositories. • Design controls that prevent unauthorized knowledge access, data leakage, and information exposure. • Implement secure handling of sensitive enterprise and healthcare data throughout AI workflows. • Perform threat modeling for AI applications, agent architectures, prompts, APIs, and retrieval systems. • Assess risks associated with prompt injection, jailbreak attempts, indirect prompt attacks, tool misuse, hallucinations, data poisoning, model abuse, and adversarial inputs. • Develop mitigation strategies that reduce AI-specific security risks while maintaining usability. • Build guardrails that improve trustworthy AI behavior. • Design policy enforcement, human-in-the-loop approval workflows, content filtering, and AI governance mechanisms. • Help define organizational standards for responsible AI development and deployment. • Design monitoring capabilities that detect abnormal agent behavior, misuse, prompt manipulation, and anomalous model interactions. • Implement logging, traceability, and audit capabilities supporting explainability and regulatory compliance. • Build mechanisms for continuous AI risk assessment and operational visibility. • Partner with software engineers to integrate AI security into development workflows. • Conduct security reviews of AI features before production deployment. • Promote secure AI engineering practices across the product organization.

🎯 Requirements

• Bachelor's degree in Computer Science, Cybersecurity, Artificial Intelligence, Software Engineering, Information Security, or a related technical discipline (or equivalent professional experience). • 7+ years of software engineering, cybersecurity engineering, AI engineering, or application security experience. • Experience designing secure cloud-native or distributed software systems. • Experience with Large Language Models (LLMs), AI applications, or Agentic AI platforms. • Experience securing APIs, microservices, and enterprise applications. • Knowledge of OWASP Top 10 and secure software development practices. • Understanding of AI-specific security risks including: • Prompt injection • Jailbreaking • Data poisoning • Model abuse • Adversarial inputs • Hallucination mitigation • Sensitive data leakage • Experience with cloud security across AWS, Azure, or Google Cloud. • Strong programming experience in Python or TypeScript. • Excellent communication and collaboration skills. • A mindset of both a security engineer and a software engineer. • Experience with solving problems that don’t yet have established playbooks. • Ability to stay current with emerging AI threats and defensive techniques. • Experience with ownership of complex technical challenges. • Ability to collaborate effectively across engineering disciplines. • Capability to influence how secure AI systems are built in highly regulated environments. • Strong ability to balance innovation with responsible engineering. • Experience securing Retrieval-Augmented Generation (RAG) systems is a plus. • Experience with AI guardrails and policy engines is a plus. • Familiarity with frameworks such as LangGraph, LangChain, CrewAI, Semantic Kernel, AutoGen, or LlamaIndex is a plus. • Experience with vector databases including Pinecone, Weaviate, Qdrant, Milvus, or Azure AI Search is a plus. • Experience implementing AI observability, evaluation, or monitoring solutions is a plus. • Experience with NIST AI Risk Management Framework (AI RMF), Responsible AI practices, or AI governance frameworks is a plus. • Experience supporting Federal Government or healthcare environments is a plus. • Familiarity with Zero Trust Architecture and secure DevSecOps practices is a plus. • Professional certifications such as CISSP, CCSP, Security+, GIAC, or cloud security certifications are a plus.

🏖️ Benefits

• Comprehensive benefits for you and your family