Founding Cloud Infrastructure Engineer (AI Platform)

About this Role We're looking for a Founding AWS Cloud Engineer to build the infrastructure that powers Valerie's AI-native platform. This is a 0→1 opportunity. There are no legacy systems to maintain or existing architectures to work around. You'll design, build, and scale our cloud infrastructure from the ground up, making the technical decisions that shape the platform for years to come. You'll work across cloud infrastructure, DevSecOps, distributed systems, and AI infrastructure, partnering closely with Product, AI, and Engineering teams to build secure, scalable, and reliable systems that support the next generation of AI-powered products. You could be a great fit if... - You enjoy building cloud infrastructure from scratch rather than maintaining legacy systems. - You have deep experience with AWS and know how to design secure, scalable, and resilient architectures. - 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.