Job Opportunities API

About AppGate AppGate secures and protects an organization's most valuable assets with its high performance Zero Trust Network Access (ZTNA) solution. AppGate is the only direct-routed ZTNA solution built for peak performance, superior protection and seamless interoperability. AppGate safeguards Fortune 500 enterprises worldwide. Learn more at appgate.com.  About the Role We're looking for a AI/ML Engineer (Senior/Staff/Principal) - Threat Detection who will design, build, and operationalize the detection algorithms, ML inference pipelines, and risk aggregation systems that power our autonomous threat detection platform.   You'll work at the intersection of identity security, behavioral analytics, and applied machine learning — building production systems that analyze ZTNA audit logs in near real-time, surface high-fidelity threat signals, and feed into our Risk Sentinel enforcement engine to continuously harden access decisions. Key Responsibilities •       Your engineering work will directly enable next-generation capabilities, including: •       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. •       ML Anomaly Detection: Production models using Isolation Forest, One-Class SVM, and Autoencoder neural networks to surface behavioral outliers that rules miss. •       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. •       Design and implement detection algorithms spanning authentication, authorization, network/location, data access, session management, and temporal behavioral domains. •       Train, evaluate, and deploy ML models on real-world identity and network telemetry; tune for production precision and recall targets. •       Architect and operate the detection pipeline — from audit log ingestion through risk aggregation and Risk Sentinel integration. •       Define the detection taxonomy — categorizing, prioritizing, and lifecycle-managing the full detection library using a scalable detection family model. •       Instrument and improve signal quality — measuring MTTD, false positive rates, and MITRE ATT&CK coverage; partnering with red teams to validate detections against real attack scenarios. •       Collaborate cross-functionally with security, product, and platform engineering to align detection coverage with customer threat models and roadmap priorities. Required Qualifications •        7+ years of production AI/ML engineering experience, w