AI Platform Engineer

Givzey

About The Position

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. 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. 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.

Requirements

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
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.
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 Haves

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

Responsibilities

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
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
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
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
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
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