Cloud Engineer

About the role

The Accelerator seeks a Cloud Engineer to design, build, and operate the secure cloud infrastructure that powers large-scale academic research on the information environment. Working as part of a small, high-trust cross-functional team, this individual will contribute across the full stack — from infrastructure and DevOps to backend services and data pipelines — and will have meaningful ownership over the technical systems that enable researchers at Princeton and across a global consortium to do their work.

Responsibilities

• Cloud Infrastructure Design, deploy, and maintain cloud infrastructure on Azure, with responsibility for performance, cost-effectiveness, and reliability across research and production environments.
• Architect and manage Databricks workspaces, including compute cluster configuration, access controls, and cost optimization for large-scale data processing workflows.
• Manage Azure networking, storage, identity (Azure AD / Entra ID), and resource governance across multiple environments.
• Implement infrastructure-as-code using Terraform and/or Bicep; maintain version-controlled, reproducible infrastructure definitions including modules, remote state management, and PR-based workflow.
• Deploy, operate, and maintain AKS clusters running containerized workloads — including containerized data crawlers — managing deploys, scaling, health monitoring, patching, and upgrades.
• Administer Azure Blob Storage, including lifecycle policies, redundancy configuration, and access tier management.
• Manage Azure networking and security, including Private Link, network rules, RBAC, and secrets hygiene across environments.
• Own Azure cost management: budget alerts, cost/cluster policies, anomaly detection and response, and FinOps practices to keep infrastructure spend predictable and efficient.
• Software Development & DevOps Design, build, and maintain backend services, APIs, and data pipelines using Python and/or TypeScript/Node.js.
• Develop and maintain CI/CD pipelines using GitHub Actions, ensuring reliable and automated delivery of infrastructure and application changes.
• Build and maintain internal tooling that improves the experience and efficiency of the research and operations teams.
• Contribute to frontend integrations where needed; comfortable working across the stack on a small team.
• Data Engineering & ML Infrastructure Develop and support data pipelines for ingesting, transforming, and serving large-scale behavioral and social media datasets to researchers.
• Implement and maintain infrastructure for machine learning workflows, including model serving, experiment tracking, and compute resource management.
• Support integration with ML frameworks and tools (e.g., MLflow, Hugging Face, or equivalent) within the managed environment.
• Security & Compliance Implement and maintain security controls across all systems, including encryption at rest and in transit, identity and access management, network segmentation, and secrets management.
• Design and operate environments meeting IRB, data governance, and institutional compliance requirements; ensure adherence to standards equivalent to SOC 2, HIPAA, or ISO 27001 as applicable.
• Conduct regular security reviews, vulnerability assessments, and penetration test coordination; manage remediation tracking.
• Implement audit logging, access controls, and data handling procedures for sensitive research data in compliance with IRB protocols and data use agreements.
• Observability & Operations Operate, patch, and upgrade the self-hosted observability stack — Grafana (dashboards), Loki (log aggregation), and Prometheus (metrics) — including security patching and version upgrades; implement and maintain alerting, distributed tracing, and platform-wide monitoring.
• Own incident response, root cause analysis, and operational reliability for production systems.
• Develop and maintain runbooks, architecture documentation, and operational procedures.

Qualifications

• Skills and Experience Required:
  • 5–8 years of experience in cloud engineering, DevOps, or a software engineering role with significant infrastructure ownership.
  • Strong proficiency in Python; experience with at least one additional language (TypeScript/Node.js, Go, or equivalent).
  • Deep hands-on experience with Azure cloud services, including compute, networking, storage, identity, and managed services; familiarity with Azure CAF landing zones, subscription governance, and resource management at scale.
  • Proficiency with Terraform, including module development, remote state management, and PR-based workflow; Bicep familiarity a plus.
  • Experience designing and implementing CI/CD pipelines, preferably using GitHub Actions.
  • Production experience with Kubernetes / AKS — deploys, scaling, health management, upgrades, and cluster operations.
  • Solid Docker and container image management skills; experience building and maintaining containerized services in production.
  • Azure networking and security fundamentals, including Private Link, network rules, NSGs, and RBAC; comfort managing secrets hygiene across environments.
  • Azure cost management and FinOps awareness: budget alerts, cost/cluster policies, anomaly detection and response.
  • Comfort operating in and improving existing codebases with limited live handoff — able to orient independently, read unfamiliar infrastructure, and contribute quickly without extensive documentation.
  • Experience with Databricks or equivalent large-scale data processing platforms.
  • Solid understanding of data security principles, IAM patterns, and compliance frameworks (SOC 2, HIPAA, ISO 27001, or equivalent).
  • Experience operating a self-hosted observability stack — specifically Grafana, Loki, and Prometheus — including patching, upgrades, and dashboard maintenance; equivalent stack experience considered.
  • Ability to work independently on complex, ambiguous problems and communicate technical decisions clearly to non-technical stakeholders.
  • Strong written communication skills; comfortable producing architecture documentation, runbooks, and technical specifications.
• Preferred Experience:
  • Supporting research computing or academic data infrastructure environments.
  • Familiarity with ML infrastructure tooling (MLflow, Hugging Face Hub, model serving frameworks).
  • Experience with IRB-compliant research data environments or sensitive data handling at scale.
  • Frontend development experience (React or equivalent) — useful on a small cross-functional team.
  • Relevant certifications: Azure Administrator (AZ-104), Azure Solutions Architect (AZ-305), Azure DevOps Engineer (AZ-400), or equivalent.

Requirements

• A combination of relevant work experience and education equivalent to 5–8 years of hands-on cloud engineering or software engineering experience, with a demonstrable record of owning and delivering complex infrastructure and software projects.
• A bachelor's degree in Computer Science, Engineering, or a related field is preferred but not required — equivalent professional experience will be considered.