AI Platform Engineer

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 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

What we're looking for

Experience

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

Cloud & Platform

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

AI Infrastructure

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.