AI Security Engineer

Location: McLean, VA | On-site (5 days/week)

Are you ready to build the security systems that protect a global enterprise automation platform from modern cyber threats? As a Security Engineer, you will design and implement advanced detection technologies to shield our critical infrastructure.

The Appian Information Security department continuously evaluates the global threat landscape to protect Appian operations and service offerings. Our mission is to build, monitor, and scale robust security tools and processes that shield critical corporate and customer assets from emerging threats, international security concerns, and vulnerabilities, ensuring our AI-Powered Process Automation platform remains resilient and secure.

At Appian, we believe our greatest innovations spring from in-person collaboration. Joining our team at our McLean headquarters means you will work alongside elite security architects in a high-velocity environment that prioritizes your professional growth. This role offers an unparalleled launchpad to transition academic knowledge into real-world impact, giving you direct ownership over intrusion detection capabilities and cloud-based defense strategies for our Enterprise-Grade Orchestration ecosystem.

• →
  Build & Deploy Agents: Design, test, and deploy autonomous and semi-autonomous AI agents that integrate natively with our enterprise security stack (SIEM, EDR, XDR, and Threat Intel feeds).
• →
  Code the Playbooks: Translate traditional, human-centric SOC playbooks and analyst workflows into deterministic and heuristic agentic pipelines (using DAGs and multi-agent routing).
• →
  Optimize RAG Pipelines: Design, optimize, and maintain production-grade Retrieval-Augmented Generation (RAG) workflows to inject real-time security context, network topology, and historical incident logs into agent prompts.
• →
  LLM Performance Engineering: Continuously evaluate, benchmark, and optimize LLM performance, context window utilization, latency, and cost-efficiency across various models (OSS and commercial).
• →
  Design Human-in-the-Loop (HITL): Collaborate deeply with Tier 3 Analysts and Threat Hunters to engineer seamless HITL handoff mechanisms, ensuring agents safely escalate complex anomalies to humans.
• →
  Secure the AI: Implement robust security boundaries around our LLM architecture, mitigating risks like prompt injection, data poisoning, model tool-abuse, and addressing the OWASP Top 10 for LLMs .

• Education: Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 4 years of experience developing AI and ML algorithms or technologies, OR a Master's degree in a related field plus at least 2 years of experience.
• Experience: At least 2 years of experience programming with Python, Go, Scala, or Java.
• You are a Builder First: You take immense pride in shipping clean, production-grade, asynchronous code. You care about system architecture as much as model accuracy.
• Security-Curious or Security-Hardened: You bridge the gap between AI research and practical cybersecurity. You understand that an AI agent is only as good as the guardrails keeping it from deleting a production server during a false positive.
• Thrives in Ambiguity: Building an Agentic SOC means charting unknown territory. You love breaking down abstract, high-level security problems into concrete, execution-ready AI systems.

• 3 years of experience deploying scalable and responsible AI solutions on cloud platforms (e.g., AWS, Google Cloud, Azure).
• Experience designing, developing, delivering, and supporting AI services, specifically within the domains of security operations or threat intelligence.
• Hands-on experience building multi-agent or complex orchestration systems using tools such as LangChain, LlamaIndex, AutoGen, CrewAI, or Semantic Kernel.
• Proven experience working with production Vector Databases (e.g., Pinecone, Qdrant, Milvus, or Weaviate) for semantic chunking, embedding generation, and metadata filtering.
• Experience deploying and scaling AI workloads in containerized cloud environments (AWS, Azure, or GCP using Kubernetes/EKS/AKS).