Staff / Principal Platform Engineer

About the role

AppGate is expanding its platform and is creating a new AI Platform & Infrastructure team, which reports to Vangie Shue, Principal Engineering Manager. The team owns the infrastructure layer behind AppGate's AI strategy, from network observability to AI-driven threat detection and the secure operation of emerging Agentic AI systems. The role combines deep DevOps and cloud infrastructure expertise with hands-on experience operationalizing AI/ML systems, and treats observability as a first-class engineering discipline. It is a hybrid position based in New York City.

Key responsibilities

• Design, build and operate the cloud infrastructure, services and pipelines for AppGate's AI and cloud products, with strong experience in self-managed technologies (Kafka, Elasticsearch) and Kubernetes
• Use Terraform and Helm for cloud provisioning, service deployment and configuration management
• Instrument APIs, cloud services and AI/ML infrastructure with metrics, logging, tracing and alerting, and define SLOs and operational health metrics
• Build real-time and batch data ingestion pipelines, feature stores and data quality
• Integrate third-party connectors, APIs and platform integrations
• Build model serving and inference pipelines, experiment tracking and MLOps tooling for deployment, versioning, drift monitoring and lifecycle management
• Apply SRE practices to reduce toil and improve resilience, and automate infrastructure, CI/CD and self-service tooling
• Set technical direction, platform standards and architecture, and mentor senior engineers
• Partner with data scientists, product teams and leadership on platform investment

Required profile

• Extensive platform, infrastructure or SRE engineering experience with production systems at scale; Staff candidates typically 8+ years, Principal 12+ years, hired on demonstrated impact
• Strong command of infrastructure-as-code, CI/CD, containers and orchestration, and cloud platforms (AWS)
• Hands-on observability experience with tools such as Prometheus, Grafana, OpenTelemetry, the ELK stack or comparable, including SLO and error-budget practice
• Familiarity with real-time and batch ingestion pipelines, feature stores and data quality at production scale
• Fluency in a primary backend language (Python, Go or similar) and a bias toward automation, testing and maintainable systems
• Record of setting technical direction, leading complex initiatives and mentoring senior engineers

Preferred qualifications

• Experience with model serving, inference pipelines and MLOps tooling such as MLflow, Kubeflow or SageMaker
• Working knowledge of TCP/IP, TLS, tunneling, DNS and firewalling, and familiarity with Zero Trust Network Access (ZTNA), VPN, SDP or SASE

What we offer

• Base pay of $185k-$225k for Staff and $215k-$270k for Principal
• Performance bonuses and considerable equity