Senior​/Principal AI​/ML Engineer - Agentic AI Security

About App Gate

App Gate secures and protects an organization's most valuable assets with its high performance Zero Trust Network Access (ZTNA) solution. App Gate is the only direct-routed ZTNA solution built for peak performance, superior protection and seamless interoperability. App Gate safeguards Fortune 500 enterprises worldwide. Learn more at

We're looking for a AI/ML Engineer (Senior/Staff/Principal) - Agentic AI Security who will design, build, and operationalize the detection algorithms, ML inference pipelines, and risk aggregation systems that power our autonomous Agentic AI threat detection platform.

You’ll work at the intersection of identity security, behavioral analytics, and applied machine learning - building an agentic AI security platform that provides end to end visibility, control and protection against rogue/malicious agents. You will build production systems that analyze ZTNA logs in near real-time, surface high-fidelity threat signals, and feed into our Risk Sentinel enforcement engine to continuously harden access decisions.

• Threat Detection Engine:
  Build advanced detections to identify threats early, including identity compromise, privilege escalation, impossible travel, and data exfiltration across identity, network, device, and session telemetry.
• Risk Aggregation & Enforcement:
  Design/develop accurate and explainable risk scoring systems that continuously normalize and correlate detection signals into dynamic user, device, and session risk scores that directly drive adaptive access enforcement decisions.
• Real-Time Detection Pipeline:
  Build scalable, low-latency streaming pipelines that process ZTNA events in near real time, enabling resilient, high-throughput security analytics.
• AI Agent Security:
  Define and implement security controls for autonomous AI agents, including detection of agent drift, unauthorized resource access, prompt injection attacks, privilege escalation, data leakage, and other emerging threats in Agentic AI systems.
• Autonomous Remediation (Roadmap):
  Leverage agentic AI to automate threat investigation, contextual analysis, and remediation workflows, enabling intelligent containment and response for high-confidence security incidents.
• Train, evaluate, and deploy ML models on real-world identity and network telemetry; tune for production precision and recall targets.

• 5+ years of production AI/ML engineering experience, with a strong preference for candidates who have built threat detection, UEBA, ITDR, or identity security platforms at leading security or cloud companies.
• Detection algorithm expertise: Hands-on experience designing detections for identity-based threats - credential compromise, privilege escalation, insider activity, behavioral anomalies, and data exfiltration.
• MLOps & Productionization: Experience building and operating scalable MLOps platforms for AI/ML systems, including model lifecycle management, CI/CD for ML pipelines, feature stores, automated retraining, model monitoring/drift detection, experiment tracking, and deployment orchestration using Kubernetes, MLflow, Kubeflow, Sage Maker, or equivalent tooling in high-throughput production environments.
• ML proficiency: Experience building AI-powered security systems using large language models, deep learning, and agentic AI techniques for threat detection, anomaly analysis, contextual investigation, and intelligent remediation.
• Data & streaming engineering: Real-time or near-real-time pipeline experience (Kafka, Flink, Spark Streaming, or equivalent); familiarity with lakehouse formats (Apache Iceberg, Parquet).
• Security domain knowledge: MITRE ATT&CK, identity threat kill chains, ZTNA or network access control systems, and…