Staff / Principal DevOps Engineer

AppGate Cybersecurity, Inc.•New York, NY

6d•$185,000 - $270,000•Hybrid

About The Position

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. We're looking for a Staff or Principal DevOps Engineer to design and build the deployment vehicle of our cloud and AI infrastructure, set the standards the rest of the team will build on, and grow the surrounding engineers into a capable platform team. You'll make the foundational decisions — cloud architecture, IaC conventions, CI/CD, observability, on-call — and then codify them so they scale beyond you. Equally important, you'll be the DevOps mentor for a team that doesn't yet have deep expertise in this area: you multiply your impact by levelling up the engineers around you, not by being the only person who can operate the system. You should be genuinely excited by a blank slate, comfortable owning ambiguity, and just as happy writing the Terraform as you are drawing the architecture and teaching someone else to extend it.

Requirements

Experience: extensive DevOps, 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.
Built from scratch: hands-on experience standing up infrastructure and CI/CD pipelines in a greenfield or early-stage environment — you have made the foundational decisions, not just operated within someone else's platform.
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.
Security mindset: familiarity with implementing security best practices in infrastructure and deployment workflows — secrets management, vulnerability remediation, and secure-by-default design.
Engineering craft: fluency in a primary backend language (Python, Go or similar) and a strong bias toward automation, testing and reliable, maintainable systems.
Mentorship & leadership: a demonstrated record of growing engineers and setting technical direction — you make the people around you better, write the docs, and lead through influence and teaching while staying very hands-on. You are comfortable being the most senior person in the room on this topic.
Mindset: pragmatic, rigorous and ownership-driven. You thrive in a small, fast-moving environment and enjoy building foundations others depend on.

Nice To Haves

Self-managed data infrastructure: experience operating Kafka, Spark, Elasticsearch, or comparable stateful systems in production.
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.
Networking & Zero Trust fundamentals: working knowledge of the network and routing layer beneath modern access solutions — TCP/IP, TLS, tunneling/overlay networks, packet routing and filtering, DNS and firewalling — and familiarity with Zero Trust Network Access (ZTNA) or adjacent domains (VPN, SDP, SASE, software-defined networking). You can reason about traffic paths, latency and throughput end-to-end, and instrument the network as a first-class observability signal.

Responsibilities

Build the Platform: Design, implement, and maintain CI/CD pipelines and self-service tooling from a greenfield starting point, enabling product teams to build, test, and deploy safely and quickly with automated guardrails, rollbacks, and paved paths.
Stand up and operate container orchestration with Docker and Kubernetes, including self-managed, stateful technologies (e.g. Kafka, Elasticsearch) running reliably in production.
Infrastructure as Code: Automate infrastructure provisioning and configuration management using Terraform and Helm, with reusable modules and patterns others can safely build on.
Observability from day one: Instrument monitoring, logging, and alerting across services and infrastructure using tools such as Prometheus, Grafana, and the ELK stack (Elasticsearch, Logstash, Kibana); define the SLOs, error budgets, and operational health metrics the team will trust.
Security Best Practices: Implement security best practices across infrastructure and deployment workflows — secrets management, least-privilege access, certificate and key management, vulnerability remediation, and secure-by-default configuration — fitting for a company whose product is security.
Mentor and level up the team: coach engineers who are strong but new to DevOps/infra, run design reviews, pair on hard problems, write the docs and playbooks, and deliberately transfer expertise so the team can own and evolve the platform without depending on any one person.
Operationalize AI/ML: Build the MLOps foundation for AppGate's AI products — model serving and inference pipelines, model deployment, versioning, and drift monitoring, and the experiment-tracking and lifecycle tooling (e.g. MLflow, Kubeflow, SageMaker) that lets data scientists ship models safely and repeatably.
Collaborate cross-functionally: partner with data scientists, product teams and leadership to align platform investment with AppGate’s strategic vision.