Senior AI Security Engineer

Appian Corporation•McLean, VA

11h

About The Position

Here at Appian, our values of Intensity and Excellence define who we are. We set high standards and live up to them, ensuring that everything we do is done with care and quality. We approach every challenge with ambition and commitment, holding ourselves and each other accountable to achieve the best results. When you join Appian, you’ll be part of a passionate team dedicated to accomplishing hard things, together.

Requirements

5+ years of experience programming with Python
2+ years of experience deploying scalable and responsible AI solutions on cloud platforms (e.g., AWS, Google Cloud, Azure)

Nice To Haves

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 interfacing with cybersecurity platforms via REST APIs/Webhooks (e.g., Splunk, CrowdStrike, Microsoft Sentinel, Palo Alto XSOAR).
Experience deploying and scaling AI workloads in containerized cloud environments (AWS, Azure, or GCP using Kubernetes/EKS/AKS)
Understanding of fine-tuning methodologies (LoRA, QLoRA) for smaller, domain-specific open-source models (e.g., Llama 3, Mistral) tailored for security log analysis
Passion for staying abreast of the latest AI research and security systems, and an ability to judiciously apply novel techniques in production.

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.