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AI Platform Engineer

GivzeyAnywhereAdded 1w ago
Platform Engineer
Full-time
Remote
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Job description

About Givzey / Version2.ai Join the Future of Fundraising at Givzey!Givzey is one of the fastest-growing and most innovative technology companies serving the nonprofit sector, on a mission to unlock more generosity through AI-powered donor engagement. At the center of that innovation is Version2.ai, the world’s first Autonomous AI fundraisers—Virtual Engagement Officers (VEOs)—designed to independently manage donor engagement and generate revenue. Unlike traditional AI tools that simply make staff more efficient, VEOs expand fundraising capacity by acting as AI workers that operate donor portfolios, build relationships, and secure gifts on their own. In just three years, Givzey’s platform has already helped organizations raise $10M+ through autonomous engagement, including individual gifts as large as $100,000. Alongside this breakthrough technology, Givzey’s Gift Agreement Platform modernizes the multi-year giving process, enabling nonprofits to secure, manage, and forecast commitments with unprecedented ease. About the role 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, and 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. One morning you might be debugging IAM permissions. That afternoon you're building a Pulumi module, improving GitHub Actions, tuning ECS workloads, or helping an ML engineer deploy a SageMaker endpoint. What you'll do Cloud infrastructure Design, build, and maintain our AWS infrastructureManage networking, IAM, compute, storage, databases, and security across environmentsBuild scalable infrastructure capable of supporting rapid product growthImprove resiliency, availability, and disaster recovery Infrastructure as Code Own our Infrastructure as Code strategy using PulumiBuild reusable infrastructure components and shared modulesEliminate manual infrastructure changes wherever possibleReview and evolve our cloud architecture as the company grows CI/CD Build and maintain deployment pipelines for applications and infrastructureImprove release automation and deployment safetyReduce friction in local development and engineering workflowsHelp establish engineering best practices around testing and deployment AI Platform Build and maintain the infrastructure powering our AI systemsWork with services such as Amazon Bedrock, SageMaker, OpenSearch, and supporting AWS servicesSupport LLM evaluation pipelines, RAG infrastructure, vector search, and model deploymentPartner with ML engineers to operationalize new AI capabilities Platform Operations Monitor production systems and improve observabilityRespond to production incidents and drive root-cause analysisImprove system reliability through automation rather than manual processesContinuously evaluate performance, cost, and scalability Engineering Collaborate closely with product, engineering, ML, and customer successHelp define technical standards and infrastructure directionParticipate in architecture discussions across the platformMentor other engineers on cloud infrastructure and operational best practices What we're looking for Experience 5+ years building and operating production software systemsStrong experience with AWS in production environmentsExperience designing Infrastructure as Code using Pulumi, Terraform, or CloudFormationExperience building CI/CD pipelines using GitHub ActionsStrong Python experienceExperience building APIs and backend systems Cloud & Platform You should be comfortable working with technologies such as: AWS (multi-account environments using AWS Organizations)ECS DockerIAMVPC networkingRDSS3LambdaCloudWatchSNS/SQSEvent-driven architectures AI Infrastructure Experience with some of the following is highly desirable: Amazon BedrockSageMakerVector databasesRetrieval-Augmented Generation (RAG)LLM evaluation pipelinesModel deploymentML infrastructureDagster 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 experienceDagster experienceAmazon BedrockSageMakerOpenSearchECSPostgreSQLRedisNew Relic or modern observability platformsExperience supporting AI or ML productsSOC 2 or security/compliance experienceStartup 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.