Staff / Principal Platform Engineer

Staff / Principal Platform Engineer

Location: New York City | Hybrid
Department: AI Platform & Infrastructure Team
Reports to: Vangie Shue - Principal Engineering Manager

About AppGate

AppGate secures and protects an organization’s most valuable assets with its high performance Zero Trust Network Access (ZTNA) solution and Cyber Advisory Services. AppGate ZTNA is the only direct-routed Zero Trust solution built for peak performance, superior protection and seamless interoperability. AppGate Cyber Advisory Services harden your security posture and ensure business continuity. AppGate safeguards enterprises and government agencies worldwide.

As we expand our platform, we are standing up a new AI Platform & Infrastructure team: the engine room of AppGate’s AI strategy. This team owns the infrastructure layer that every next-generation security capability is built on, from network observability to AI-driven threat detection and the secure operation of emerging Agentic AI systems.

This is a rare opportunity to join a small, private, high-impact company where your work directly shapes the architecture, reliability and core platform that defines the future of security. 

About the Role

We’re looking for a Staff or Principal Platform Engineer to build and operate the foundational platform behind AppGate’s AI products. You combine deep DevOps and cloud infrastructure expertise with hands-on experience operationalizing AI/ML systems, and you treat observability as a first-class engineering discipline.

You’ll own the platform spanning APIs, cloud services and AI/ML infrastructure, and you’ll make it fast, reliable and observable at scale. This is a high-leverage, hands-on role for a senior engineer who sets technical direction and still ships.

Key Responsibilities

• Integrations: third-party connectors, APIs and platform integrations.
• Data Platform: real-time and batch data ingestion pipelines, feature stores and data quality.
• AI/ML Infrastructure: model serving, inference pipelines and experiment tracking.
• MLOps: model deployment, versioning, drift monitoring and lifecycle management.
• Platform Observability: SLOs, operational health metrics and reliability tooling for AI systems.
• Build the platform: design, build and operate the cloud infrastructure, services and pipelines that AppGate’s AI products run on.
• Implement observability: instrument APIs, cloud services and AI/ML infrastructure with metrics, logging, tracing and alerting, and define SLOs and operational health metrics that teams trust.
• Operationalize AI/ML: build model serving and inference pipelines, experiment tracking and the MLOps tooling for deployment, versioning, drift monitoring and lifecycle management.
• Engineer for reliability: apply SRE practices to reduce toil, improve resilience and keep latency and uptime within target across the platform.
• Automate everything: drive infrastructure-as-code, CI/CD and self-service tooling so product teams ship safely and quickly.
• Set technical direction: define platform standards, architecture and best practices, and raise the engineering bar through design reviews and mentorship.
• Collaborate cross-functionally: partner with data scientists, product teams and leadership to align platform investment with AppGate’s strategic vision.

Required Qualifications

• Experience: extensive platform, infrastructure or SRE engineering experience, with a track record of operating production systems at scale. Staff-level candidates typically bring 8+ years and Principal-level candidates 12+ years, though we hire on demonstrated impact.
• DevOps depth: strong command of cloud platforms (AWS, GCP or Azure), containers and orchestration (Docker, Kubernetes), infrastructure-as-code (Terraform or equivalent) and CI/CD.
• Observability expertise: hands-on experience implementing observability across APIs, cloud services and distributed systems using tools such as Prometheus, Grafana, OpenTelemetry, the ELK stack or comparable, including SLO and error-budget practice.
• AI/ML infrastructure: experience building or operating model serving, inference pipelines and MLOps tooling such as MLflow, Kubeflow, SageMaker or equivalent, including model deployment, versioning and drift monitoring.
• Data platform skills: familiarity with real-time and batch ingestion pipelin