• As a Senior Cloud DevOps Engineer at MBI, you will take a hands-on role in designing, building, and maintaining our cloud infrastructure across Microsoft Azure and Amazon Web Services. • You will work in close partnership with the CTO, VP of Product, VP of Infrastructure, and CISO to ensure cloud platforms align with product delivery timelines, maintain operational excellence, and enforce security best practices. • Ensure fast, secure, and reliable software delivery through automation and DevOps best practices. • Manage multi-cloud environments on Azure and AWS, covering compute, storage, networking, and platform services. • Develop and maintain robust CI/CD pipelines to automate software build, test, and deployment processes. • Employ Infrastructure as Code (IaC) tools to automate provisioning and configuration of cloud resources. • Monitor and analyze system performance metrics, ensuring scalability, reliability, and cost optimization. • Work directly with the CISO and security team to ensure cloud environments meet security requirements and compliance standards.

• Create and foster Self-Service Principles - create automated process for creating on-demand platform for deploying containerized applications and exposing API endpoints to federal data consumers • Architectural Vision & Strategy: Shape and communicate the architectural blueprint for Lighthouse with a focus on scalability, security, automation, and developer experience in a cloud-native environment • Stakeholder Engagement: Collaborate with product owners, security teams, and stakeholders to facilitate and prioritize features that Program Manager(s) can use to meet business needs and create technical roadmaps, ensuring compliance with VA, federal, and industry requirements • Establish and Enforce Standards: Define, document, and advocate for engineering best practices across the VA Lighthouse program, leveraging your expertise in DevSecOps and Kubernetes • Platform as a Service (PaaS): Provide clients with PaaS solutions—delivering low-code/no-code development platforms and enterprise-grade API engineering to accelerate application delivery, streamline integration, and enable scalable digital solutions • End-to-End Software Development: Execute efforts across the full software development lifecycle, automating processes, boosting operational efficiency, and strengthening system reliability • DevSecOps Ownership: Design, implement, and maintain secure, automated CI/CD pipelines and increased monitoring and platform related business intelligence • Automate build, test, deployment, and infrastructure processes; champion shift-left security practices • Continuous Innovation: Continuously refine the platform and deliver innovative, reliable solutions to meet the mission • Governance & Compliance: Ensure solutions meet all compliance requirements, including audit readiness and documentation

• Manage and maintain a globally distributed blockchain infrastructure fleet • Design, architect, deploy, and operate production-grade infrastructure services • Implement and maintain infrastructure-as-code across development, staging, and production environments • Ensure high availability and performance of mission-critical systems • Contribute to automation, CI/CD pipelines, and operational tooling • Monitor system health and respond to incidents with strong troubleshooting fundamentals • Uphold the highest standards of integrity, professionalism, and operational discipline

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description To build and maintain the automated production line for PHIN's Physical Superintelligence. You will own the plumbing that allows our simulation engine to seamlessly scale, ensuring that our team can deploy updates multiple times a day and ingest massive amounts of simulation data without friction. - Greenfield Observability: Architect and implement a comprehensive logging, monitoring, and alerting stack across our platform from the ground up. - Compute Architecture, Scaling & FinOps: Provision, manage, and optimize highly concurrent scaling clusters. Act as a cloud-agnostic thinker to direct future architecture and implement rigorous FinOps practices to minimize the cost of running thousands of simultaneous jobs. - Infrastructure as Code (IaC): Own, maintain, and expand our Terraform footprint. - Continuous Deployment (CD): Design and maintain high-velocity CI/CD pipelines supporting multiple deployments per day. Ensure "code to production" is a seamless, automated journey. - Backend Robustness: Manage the API layer that sits between the infrastructure and the application layer. Read and refactor services to optimize data movement, squash bottlenecks, and maintain security. - Data Pipeline Architecture: Build the underlying pipelines to move, store, and process the massive datasets generated by atomic-scale simulations. - Platform DevEx & MLOps: Build self-serve tooling and event-driven pipelines that empower the entire organization. Create seamless abstractions so our developers can focus on what they do best. - DevOps & Intelligence Automation: Ruthlessly automate manual toil. Use and build AI-driven tools to manage logs, infrastructure provisioning, and business workflows. - Standard Enterprise Security: Implement and maintain security best practices (SOC2/ISO focus) required for enterprise-grade contracts. Qualifications - 5–8 years as a high-output Individual Contributor in Infrastructure or Backend roles. - Comfortable touching any part of the system—from networking and security to API design and data engineering. - Familiarity with Python and TypeScript/Node.js. - Deep experience with major cloud providers. - Familiarity with high-performance computing (HPC) schedulers like Slurm is a major plus. - Not married to one framework; you choose the best tool for the job (K8s, Serverless, HPC Schedulers, etc.). - Expert user of intelligence tools (Claude, Cursor, Codex, Copilot, Agents, etc.) to 10x your own productivity and automate business tasks. - Previous experience working closely with machine learning teams, supporting ML workflows, or building MLOps pipelines is highly desirable.