Founding Cloud Infrastructure Engineer (AI Platform)

You think in systems and enjoy making technical decisions that will support long-term growth.

You're comfortable owning everything from Infrastructure as Code and CI/CD pipelines to observability and production reliability.

You enjoy working closely with engineers, AI teams, and product teams to bring new ideas into production.

You're passionate about automation and believe infrastructure should be repeatable, secure, and easy to scale.

You understand the unique challenges of AI-native applications and enjoy building the infrastructure that powers them.

You're proactive, take ownership, and don't wait for someone else to solve problems.

Requirements

Strong hands-on experience building and managing production infrastructure on AWS.

Deep knowledge of core AWS services, including EC2, ECS, VPC, IAM, S3, RDS, Route 53, CloudFront, CloudWatch, and Load Balancers.

Experience with Infrastructure as Code using Terraform (preferred) or AWS CloudFormation.

Strong experience with Docker, containerized environments, and CI/CD pipelines.

Solid understanding of Linux, networking, monitoring, debugging, and production incident management.

Experience designing secure cloud environments, IAM policies, secrets management, and infrastructure security best practices.

Experience building scalable distributed systems and event-driven architectures.

Experience supporting AI or machine learning infrastructure, including LLM workloads, vector databases, or retrieval-based systems.

Strong communication skills and the ability to explain technical trade-offs to both technical and non-technical stakeholders.

Bonus Points

Experience with Kubernetes, Amazon ECS, or large-scale container orchestration.

Familiarity with serverless technologies such as Lambda, EventBridge, or Step Functions.

Experience with Redis, Kafka, SQS, or other messaging and caching systems.

Experience with AI infrastructure frameworks such as LangChain, LangGraph, or LlamaIndex.

Experience with vector databases such as Pinecone, Weaviate, Qdrant, or pgvector.

Experience with GPU infrastructure, MLOps, or AI inference systems.

Experience working in an early-stage startup or building products from zero to one.

You probably shouldn't apply if...

You prefer maintaining existing infrastructure over designing new systems.

You rely on predefined architecture rather than making technical decisions independently.

You're uncomfortable working in fast-moving environments where priorities evolve.

You prefer narrowly defined responsibilities over end-to-end ownership.

You're looking for a role focused primarily on operations rather than architecture, automation, and platform engineering.

The best time to join Valerie was yesterday. The next best time is now 🤓

👋Our Hiring Process

We want interviews to be valuable for both sides. Throughout the process, you'll meet the people you'll work with, learn more about the role, and get a chance to understand how we think and operate.

Our process typically includes:

Introductory conversation with our Talent team <> 30 mins

Hiring Manager interview <> 30-45 mins

Role-specific assessment or practical exercise (where applicable)

Cross-functional or stakeholder interviews <> 30-45 mins

Final conversation with leadership <> 30-45 mins

We'll always let you know what to expect before each stage.

💪🏾What You Can Expect

Meaningful work with visible impact

High ownership from day one

Collaboration with experienced founders, operators, and specialists

A team that values curiosity, initiative, and continuous improvement

Competitive compensation and the tools you need to do your best work

Opportunities to grow as Valerie grows

Valerie is committed to building an inclusive workplace where people from different backgrounds, experiences, and perspectives can thrive. We hire based on skills, potential, and the ability to contribute to our team.