AI Security Engineer

Appian Corporation•McLean, VA

13d

About The Position

In this role, you will build and deploy AI agents that integrate with our enterprise security stack. You will code playbooks to translate human-centric SOC workflows into agentic pipelines, optimize Retrieval-Augmented Generation (RAG) workflows, and engineer LLM performance. You will also design human-in-the-loop mechanisms for escalating anomalies and implement security boundaries around the LLM architecture to mitigate risks.

Requirements

Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 2 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.
At least 2 years of experience programming with Python, Go, Scala, or Java.

Nice To Haves

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).

Responsibilities

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).
Translate traditional, human-centric SOC playbooks and analyst workflows into deterministic and heuristic agentic pipelines (using DAGs and multi-agent routing).
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.
Continuously evaluate, benchmark, and optimize LLM performance, context window utilization, latency, and cost-efficiency across various models (OSS and commercial).
Collaborate deeply with Tier 3 Analysts and Threat Hunters to engineer seamless Human-in-the-Loop (HITL) handoff mechanisms, ensuring agents safely escalate complex anomalies to humans.
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.