Top Remote Infrastructure Engineer Jobs

Design, build, and operate secure, scalable cloud infrastructure for a blockchain wallet backend. Lead automation with Terraform and CI/CD, run containerized workloads (Docker, Kubernetes), implement observability (Datadog), tune SQL/NoSQL databases, optimize Node.js services, configure secure enclaves, and align infrastructure with product and security teams.

Design and maintain infrastructure to support high-performance APIs and data services at Polymarket. Lead system architecture and enhance development workflows, ensuring reliability and scalability.

The Sr. Infrastructure Engineer will architect and maintain scalable infrastructure, lead incident management, improve operational processes, and mentor junior engineers.

Design, implement, and manage LiveKit's infrastructure, focusing on performance, reliability, and automation while ensuring effective incident management and coordination with vendors.

Design and maintain cloud infrastructure on GCP, manage Kubernetes, improve CI/CD pipelines, ensure reliability and security, and support rapid development cycles.

The Infrastructure Engineer will scale and automate infrastructure, improve observability and reliability, and support engineering teams through data-driven decisions.

The role involves designing and implementing MLOps pipelines, optimizing machine learning processes, and ensuring model performance and compliance in a collaborative environment.

Lead the reliability and operations for ClickHouse's Postgres integration, automate deployments, develop infrastructure-as-code, and mentor engineers.

Operate, maintain, and improve secure, highly available server, network, and middleware infrastructure. Install, configure, monitor, troubleshoot, automate, and document systems; support on-call rotation and disaster recovery; manage job schedulers and vendor relationships; plan capacity and upgrades; mentor junior staff and support multi-cloud workloads.

Design, build, and operate scalable batch and streaming data pipelines and a lakehouse on AWS. Implement ETL/ELT, CDC, metadata/catalog and governance, automated AWS provisioning (IaC), CI/CD for data pipelines, data quality and lineage, observability, and documentation while collaborating across teams.

Manage and deliver data center IT infrastructure projects (GPU servers, networking, storage). Coordinate stakeholders, lead technical discussions, resolve operational issues, document plans and risks, and support automation and process improvements.

Support and optimize large-scale Spark/YARN jobs and storage (S3/EMR). Build job-profiling, cost-analysis, AI-assisted automation, monitoring/alerting (Prometheus, Flink/Spark), and internal ops tooling to improve stability and reduce costs.

Own and build Grow Therapy's data security infrastructure: automated data classification, field-level masking/tokenization, encryption and key management, secure data connectors to AI tooling, and access controls. Define a multi-year vision, implement pipelines and services, and partner across Data, Engineering, and Detection & Response to make secure-by-default the company standard.

As an ML Infrastructure Engineer, you'll develop platforms and solutions for running ML jobs efficiently, establish CI/CD practices, and lead initiatives in technology alignment.

As a Senior Infrastructure Engineer, you'll own the CI/CD platform, AWS infrastructure, and improve developer experience at Rally, driving scalable growth.

The Infrastructure Engineer is responsible for building and maintaining deployment automation, CI/CD pipelines, and ensuring the operational health of production deployments across federal environments, focusing on automation and compliance.

As a Database & Infrastructure Engineer, you will optimize PostgreSQL infrastructure, improve performance, build internal tools, and design data delivery pipelines for a large-scale application.

The Staff Cloud Infrastructure Engineer is responsible for designing, automating, and managing cloud infrastructure using AWS and tools like Terraform. This role involves collaborating with engineering teams, ensuring security, and mentoring others, all while building reliable and scalable systems.

Seeking a senior or staff level infrastructure engineer to build and maintain scalable infrastructure for AI software development, including app hosting and SDK integration.

The role involves designing and advancing infrastructure for the engineering team, ensuring the reliability of Kubernetes clusters, automating operations, and building machine learning infrastructure.

Design and implement a virtual access layer to enable remote use of a physical AI lab, building scalable, secure infrastructure to process 100,000+ documents/day with LLMs, optimize GPU/LLM throughput, provide customizable interfaces, implement authentication/access controls, and lead projects from design to production.

Design, implement, and maintain global hybrid infrastructure integrating Azure, Microsoft 365, and on-prem Windows. Administer identity, endpoint management (Intune, Jamf, SCCM), Azure IaaS, networking, core services (AD, DNS, DHCP), monitoring, and patching. Lead governance, documentation, disaster recovery, mentor junior engineers, and participate in on-call rotation while ensuring security, scalability, and operational excellence.

Build and maintain cloud-native infrastructure on GCP: own CI/CD, Terraform modules, Kubernetes/Helm deployments, observability consolidation, resilience and security improvements, and automate routine ops with AI agents while supporting application teams and SOC 2 readiness.

Design, engineer, and support enterprise Red Hat Linux (RHEL) environments across physical, virtual, and cloud platforms. Lead OS lifecycle management, automation (Ansible, scripting), security hardening, and compliance. Troubleshoot complex OS, performance, storage, network, and application issues. Support virtualization and container platforms, serve as senior escalation, mentor junior engineers, participate in on-call rotations, and drive infrastructure modernization.

Deploy, operate, and debug AI systems inside client infrastructure. Own production readiness, networking, observability, cost controls, IaC (Terraform/Pulumi), CI/CD and stakeholder management with client IT/DevOps to ensure scalable, governable AI deployments.