Senior AI Platform Engineer

Senior Platform Engineer Mountain View, US About EarnIn As one of the first pioneers of earned wage access, our passion at EarnIn is building products that deliver real-time financial flexibility for those with the unique needs of living paycheck to paycheck. Our community members access their earnings as they earn them, with options to spend, save, and grow their money without mandatory fees, interest rates, or credit checks. We're fortunate to have an incredibly experienced leadership team, combined with world-class funding partners like A16Z, Matrix Partners, DST, Ribbit Capital, and a very healthy core business with a tremendous runway. We're growing fast and are excited to continue bringing world-class talent onboard to help shape the next chapter of our growth journey. Position Summary We are the Platform-as-a-Service Engineering team at EarnIn. We build the foundational "Paved Road" that enables our product teams to ship with velocity and safety. Our stack is built on AWS, EKS, and ArgoCD, but our future is Agentic. We are shifting from manual configuration to AI-augmented orchestration, where the platform anticipates developer needs and self-heals through intelligent automation. We're seeking a Senior Platform Engineer to design, build, and operate the agentic systems and core infrastructure that power our platform. You will work across Kubernetes (AWS EKS), GitOps (Argo CD), CI/CD (GitHub Actions), and our developer portal (Cortex), building AI agents and automation that reduce operational toil and increase platform leverage for engineering teams across EarnIn. As a Senior engineer, you independently design and implement solutions for well-scoped but complex problems, re-architecting components, adopting new technologies, and improving existing processes with minimal guidance. You'll build agents that handle real slices of incident triage, environment provisioning, config generation, and developer self-service, and you'll partner with Staff engineers and your manager to align your work with the team's broader technical direction. The base salary range for this full-time position is $228,000-$279,000, plus equity and benefits. Our salary ranges are determined by role, level, and location. This is a hybrid position in Mountain View (Headquarters) and will require in-office work 2 days a week. What You'll Do AI-Agent Design and Build: Design, build, and operate AI agents in production, following established governance patterns for model selection, evaluation, and safety. Contribute to infrastructure-as-code practices for agentic systems, ensuring prompts, tools, and evaluation criteria are versioned, reviewed, and tested like any other critical component. Technical Execution: Implement agentic patterns for cloud infrastructure in your area of ownership, applying and helping refine team best practices for production AI agents. Support and mentor less experienced engineers on agentic patterns, LLM integration, and prompt engineering as opportunities arise. Platform Operations: Operate and improve high-availability distributed systems on AWS, identifying and resolving performance, scalability, and stability issues within your domain. Use AI-driven observability and anomaly detection to catch problems early. Evolve infrastructure-as-code and automation standards for the services you own, incorporating agentic pattern recognition and automated remediation where it adds value. Developer Platform: Contribute to the evolution of our developer control plane (Cortex) as an AI-augmented self-service platform, building features that let engineers scaffold services, debug deployments, and resolve issues through natural language. Help encode platform standards and security policies into AI-powered golden paths. Reliability and Documentation: Document agentic architecture, best practices, and operational procedures for the systems you build. Participate in on-call rotations, and use post-mortems as a feedback loop to improve system reliability and your team's agentic automation. What We're Looking For Bachelor's or Master's degree in Computer Science, Engineering, or related field. 4+ years in cloud infrastructure, working on large-scale, high-availability, customer-facing distributed systems. Experience mentoring junior engineers and leading well-scoped platform initiatives within your team. Hands-on experience building and operating AI-driven systems in production, including agentic workflows that perform operational tasks. You should be able to speak to specific agent designs or automation you've built. Experience reducing operational toil through agentic AI (e.g., LLM-powered incident diagnosis, intelligent CI/CD with test selection, self-service assistants) on at least one meaningful workflow. Strong working knowledge of AWS (EKS, Lambda, Bedrock, etc.) and experience with containerized and serverless architectures. Solid expertise in Kubernetes at scale and ability to implement complex, resilient solutions with guidance from senior/staff engineers. Strong knowledge of infrastructure-as-code tools (Terraform, Ansible) and experience applying both traditional IaC and agentic automation. Strong command of Datadog and observability practices, using metrics to drive decisions. Experience building AI-driven alerting or root-cause analysis systems is a plus. Strong adherence to security, privacy, and compliance best practices, with awareness of governance considerations for production AI systems (model safety, prompt injection prevention, data isolation). Experience with LLM orchestration frameworks (LangChain, LlamaIndex, CrewAI, or custom agentic architectures) and production prompt engineering. Strong coding ability in Python and/or Go, with experience treating infrastructure and agentic systems as software. Ability to collaborate effectively across engineering, product, and security teams on cross-functional work. Experience with service mesh (Linkerd, Istio) and traffic management at scale is a plus. Proficiency with GitOps (Argo CD, Flux CD) and CI/CD orchestration (GitHub Actions, Argo Workflows) is a plus. Experience with MLOps or LLMOps concepts (model versioning, evaluation frameworks, production monitoring for AI systems) is a plus. Familiarity with security frameworks relevant to AI systems (guardrails, audit logging, data governance for LLMs) is a plus. Earnin