8+ years of platform, infrastructure, or SRE engineering experience for Staff; 12+ years for Principal.
Proficient in cloud platforms (AWS, GCP, or Azure), containers (Docker, Kubernetes), and infrastructure-as-code (Terraform).
Experience in building observability for APIs and distributed systems using tools like Prometheus, Grafana, and ELK stack.
Hands-on experience with AI/ML infrastructure, including model serving and MLOps platforms such as MLflow or Kubeflow.
Familiarity with data ingestion pipelines and data quality at scale.
Strong programming skills in backend languages (Python, Go, etc.).
Proven ability to lead technical direction and mentor engineers.
Responsibilities
Integrate third-party connectors, APIs, and platform integrations.
Build real-time and batch data ingestion pipelines and ensure data quality.
Develop model serving and inference pipelines for AI/ML applications.
Implement MLOps practices for model deployment and lifecycle management.
Establish platform observability with metrics and operational health assessments.
Design and operate cloud infrastructure and services for AI products.
Automate with infrastructure-as-code, enhance CI/CD, and create self-service tooling.
Benefits
Hybrid work arrangement in New York City.
Opportunity to influence technical direction and design.
Collaboration with cross-functional teams, including data scientists and product teams.
Focus on operationalizing cutting-edge AI technologies.
Full Job Description
Staff / Principal Platform Engineer
Location: New York City Hybrid Department: AI Platform & Infrastructure Team Reports to: Vangie Shue - Principal Engineering Manager
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 pipelines, feature stores and data quality at production scale.
Engineering craft: fluency in a primary backend language (Python, Go or similar) and a strong bias toward automation, testing and reliable, maintainable systems.
Leadership: a record of setting technical direction, leading complex initiatives across teams and mentoring senior engineers.
Mindset: pragmatic, rigorous and ownership-driven. You thrive in a small, fast-moving environment and enjoy building foundations others depend on.
Compensation
Staff: 185k-225k base
Principal: 215k-270k base
We offer performance bonuses and considerable equity.
