Role Description We are seeking a skilled Azure Cloud Engineer to design, deploy, and operate large-scale, secure, and resilient cloud platforms on Microsoft Azure. In this role you will own the end-to-end cloud engineering lifecycle, including: - Architecture - Infrastructure-as-code - Automation - Security hardening - Cost optimization - Observability - Ongoing operational excellence for production workloads The ideal candidate will combine deep technical expertise across Azure services with strong DevOps engineering practices, and will partner closely with application development, security, and SRE teams to deliver cloud-native solutions that meet demanding business requirements for scalability, reliability, and compliance. Qualifications - Bachelor’s degree in Computer Science, Engineering, or a related technical discipline. - Five or more years of cloud engineering experience, with at least three years focused on Microsoft Azure in production environments. - Strong hands-on experience with Azure core services, including compute, storage, networking, identity, and platform-as-a-service offerings. - Production-level experience with infrastructure-as-code tools such as Terraform, Bicep, or ARM templates. - Solid experience designing and operating Azure Kubernetes Service (AKS) clusters at scale. - Hands-on experience with Azure DevOps or GitHub Actions for CI/CD across infrastructure and applications. - Strong scripting skills in PowerShell, Bash, and Python, with the ability to write maintainable automation code. - Deep understanding of cloud security principles, identity management, and compliance frameworks. - Experience implementing monitoring, alerting, and observability strategies across distributed workloads. - Strong troubleshooting, communication, and documentation skills. Requirements - Design and implement enterprise-grade Azure cloud architectures spanning compute, networking, storage, identity, and data services, with explicit attention to scalability, security, and total cost of ownership. - Develop, maintain, and continuously improve infrastructure-as-code using Terraform, Bicep, or ARM templates. - Configure and manage Azure landing zones, virtual networks, subnets, route tables, and network security groups. - Implement secure identity, access management, and governance controls using Azure Active Directory. - Architect and operate Azure Kubernetes Service (AKS) clusters. - Deploy, scale, and tune Azure data and analytics platforms. - Build and operate comprehensive CI/CD pipelines using Azure DevOps or GitHub Actions. - Design and implement robust observability practices using Azure Monitor and other tools. - Drive Azure cost optimization initiatives. - Implement disaster-recovery and business-continuity strategies. - Strengthen security posture by integrating Microsoft Defender for Cloud and other tools. - Collaborate closely with application teams to architect cloud-native solutions. - Develop automation scripts and tooling in PowerShell, Bash, and Python. - Mentor junior engineers and contribute to internal communities of practice. Benefits - Competitive base salary commensurate with experience, plus benefits. - Long-term, multi-year engagement aligned to the Bright Vision SOW delivery roadmap. - 100% remote work opportunity.

Role Description This role owns the platform that keeps Givzey secure, compliant, reliable, and scalable. You'll work across AWS infrastructure, Infrastructure as Code, CI/CD, AI services, observability, and developer tooling to make sure engineers spend their time building product instead of fighting deployments. You'll partner closely with engineering, ML, and product to design the platform that powers everything from customer-facing APIs to LLM workflows running on Amazon Bedrock and SageMaker. This is not a "keep the lights on" devops role. You'll actively shape how we deploy software, provision infrastructure, manage AI workloads, and scale the engineering organization. Who thrives here - You're the engineer who gets excited about replacing a manual deployment with a one-click pipeline. - Automating infrastructure instead of clicking around the AWS console. - Designing systems that make the rest of engineering move faster. - You think in terms of reliability, observability, automation, and repeatability. - You're comfortable wearing multiple hats. What you'll do - Cloud infrastructure - Design, build, and maintain our AWS infrastructure. - Manage networking, IAM, compute, storage, databases, and security across environments. - Build scalable infrastructure capable of supporting rapid product growth. - Improve resiliency, availability, and disaster recovery. - Infrastructure as Code - Own our Infrastructure as Code strategy using Pulumi. - Build reusable infrastructure components and shared modules. - Eliminate manual infrastructure changes wherever possible. - Review and evolve our cloud architecture as the company grows. - CI/CD - Build and maintain deployment pipelines for applications and infrastructure. - Improve release automation and deployment safety. - Reduce friction in local development and engineering workflows. - Help establish engineering best practices around testing and deployment. - AI Platform - Build and maintain the infrastructure powering our AI systems. - Work with services such as Amazon Bedrock, SageMaker, OpenSearch, and supporting AWS services. - Support LLM evaluation pipelines, RAG infrastructure, vector search, and model deployment. - Partner with ML engineers to operationalize new AI capabilities. - Platform Operations - Monitor production systems and improve observability. - Respond to production incidents and drive root-cause analysis. - Improve system reliability through automation rather than manual processes. - Continuously evaluate performance, cost, and scalability. - Engineering - Collaborate closely with product, engineering, ML, and customer success. - Help define technical standards and infrastructure direction. - Participate in architecture discussions across the platform. - Mentor other engineers on cloud infrastructure and operational best practices. Qualifications - 5+ years building and operating production software systems. - Strong experience with AWS in production environments. - Experience designing Infrastructure as Code using Pulumi, Terraform, or CloudFormation. - Experience building CI/CD pipelines using GitHub Actions. - Strong Python experience. - Experience building APIs and backend systems. Requirements - You should be comfortable working with technologies such as: - AWS (multi-account environments using AWS Organizations) - ECS - Docker - IAM - VPC networking - RDS - S3 - Lambda - CloudWatch - SNS/SQS - Event-driven architectures - Experience with some of the following is highly desirable: - Amazon Bedrock - SageMaker - Vector databases - Retrieval-Augmented Generation (RAG) - LLM evaluation pipelines - Model deployment - ML infrastructure - Dagster or similar orchestration platforms Working Style - You automate repetitive work instead of documenting it. - You care about reliability as much as shipping features. - You enjoy improving developer experience. - You think systems should become simpler over time. - You take ownership rather than waiting for someone else to fix infrastructure problems. Mindset - Strong written communication. - Comfortable working in ambiguity. - Curious about modern AI infrastructure and where it's headed. - Interested in building systems that engineers enjoy working in. - Excited by the challenge of building infrastructure from the ground up rather than inheriting a mature platform. Nice to have - Pulumi experience - Dagster experience - Amazon Bedrock - SageMaker - OpenSearch - ECS - PostgreSQL - Redis - New Relic or modern observability platforms - Experience supporting AI or ML products - SOC 2 or security/compliance experience - Startup experience What this isn't - This isn't a traditional DevOps role where tickets get tossed over the wall after development. - This isn't an SRE role focused exclusively on uptime. - This isn't an ML engineering role building models. - You're building the platform that allows all of those disciplines to move faster. - You'll own infrastructure decisions, improve how software gets delivered, and help shape the technical foundation of an AI company that's still early enough for your decisions to matter years from now.

• You will design and build the foundational development & infrastructure powering our agentic security platform — secure multi-tenant systems, distributed services, scalable execution layers, and the internal platform capabilities that allow our AI agents to operate reliably in production environments. • You’ll work closely with founders, product, AI, and security engineering teams to build systems that are secure, observable, resilient, and scalable from day one. • Own end-to-end design and delivery of platform capabilities. • Build and scale distributed backend services powering AI agents and customer workflows. • Improve authentication, authorization, and tenant isolation across the platform. • Design infrastructure and observability systems for reliability at scale. • Deploy and operate production-grade systems with strong ownership beyond launch. • Collaborate cross-functionally with engineering, product, customer-facing teams, and leadership to shape platform architecture and engineering standards. • Build high-scale, multi-tenant backend systems that securely support multiple customer environments. • Design and operate distributed microservices with strong reliability and fault tolerance. • Build asynchronous execution pipelines, event-driven architectures, and workflow orchestration systems. • Improve scalability, throughput, concurrency, and system performance across services. • Design resilient APIs, service communication patterns, and infrastructure abstractions.

• Contribute to building and designing the self-service platform • Architect and build self-service infrastructure frameworks • Codify and abstract AWS footprint into reusable infrastructure-as-code modules • Build and implement evaluation frameworks and observability practices • Evaluate ecosystem needs holistically to spot platform investments • Align architectural deliverables with application workflows • Provide technical escalation availability during agreed windows