Note: The job is a remote job and is open to candidates in USA. AppGate secures organizations' valuable assets with a Zero Trust Network Access solution. The AI/ML Engineer will design, build, and operationalize threat detection algorithms, machine learning inference pipelines, risk aggregation systems, and security controls for autonomous AI agents. The role also involves developing real-time security analytics, deploying ML models, and enabling autonomous investigation and remediation workflows.
Responsibilities
- 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
Skills
- 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, SageMaker, 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 audit log analysis
- **Mindset:** Mission-driven, production-focused, signal-obsessed. You measure precision and recall, you eliminate alert fatigue, and you care that your work protects real systems
Benefits
- Remote work arrangement
Company Overview
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