At Quantum Circuits Inc., we are building the world’s first truly algorithmic quantum computers to achieve transformational computing capabilities. Our full-stack quantum computing platform uses dual-rail superconducting devices along with a modular, robust, and scalable architecture. Our unique approach is based on a decade of technology research and breakthroughs at Yale University's world-renowned quantum labs.
We are seeking a highly skilled and self-motivated Senior Cloud Architect to join our dynamic team. The ideal candidate has a strong background in leading the design, implementation, and continuous improvement of our cloud infrastructure. This is a senior, hands-on role ideal for a systems thinker who can architect complex distributed systems, guide modernization efforts, and contribute directly to high-priority cloud initiatives.
Our cloud-based applications frequently interface with specialized hardware platforms, including embedded systems and scientific instrumentation, and support a range of data-intensive workloads. While direct experience with these systems isn't required, a strong conceptual understanding of Linux internals, embedded environments, or FPGA-based architectures will enable you to architect more effective, integrated solutions. You'll be expected to work independently, lead projects from concept through delivery, and collaborate cross-functionally to align technical solutions with organizational objectives.
Responsibilities / Description:
Design and implement secure, scalable, and maintainable cloud architectures to support a variety of internal applications and workflows. Continuously assess and improve existing systems to reduce complexity, boost performance, and increase reliability. Lead full lifecycle delivery of technical initiatives — from design through hands-on implementation, documentation, and internal knowledge transfer. Define and enforce cloud security, observability, and operational readiness best practices across environments. Translate complex technical and business requirements into cloud-native, scalable solutions. Understand CI/CD pipelines, Docker-based containerization (or comparable technologies), and infrastructure automation; able to collaborate with DevOps engineers, identify opportunities for improvement, and contribute directly when needed. Contribute to Python-based tools, infrastructure code, or reference implementations that support architectural goals.
Minimum Qualifications:
7+ years of experience in software, infrastructure, or cloud engineering, with significant time spent designing and delivering AWS-based architectures Proven track record of leading complex technical projects from planning through production deployment Deep understanding of AWS services, including IAM, VPC, EC2, Lambda, API Gateway, CloudWatch, and S3 Proficiency in at least one modern programming language (preferably Python), including developing and consuming RESTful APIs Familiarity with container-based deployment strategies and tools (e.g., Docker), including defining and managing container images Understanding of CI/CD workflows, infrastructure automation, and Infrastructure-as-Code using Terraform or CloudFormation Strong grasp of security, observability, and operational best practices for cloud-native applications Ability to collaborate effectively across technical teams, translate business needs into technical designs, and drive architecture to completion
Preferred Qualifications:
10+ years of experience in software, infrastructure, or cloud engineering, with significant time spent designing and delivering AWS-based architectures, bonus points for experience with Braket Experience with cloud interface to any of the following Quantum environments: Azure Preview, Qiskit, cuda-Q Experience designing cloud architectures that interact with hardware platforms, embedded systems, or data acquisition environments Strong conceptual understanding of Linux internals, including custom OS builds, low-level debugging, or supporting non-standard hardware environments Familiarity with FPGA-based devices, embedded compute modules, or virtualization platforms used for scientific or control systems Ability to bridge hardware-generated data and cloud-native applications, such as designing data platforms or pipelines that integrate on-prem sources with simulation, analytics, or machine learning workflows Understanding of how to ingest, structure, and monitor telemetry, logs, and diagnostics from distributed systems across cloud and on-prem environments 