Senior Platform Engineer

EarnIn•Mountain View, CA

12d•$228,000 - $279,000•Hybrid

About The Position

EarnIn is seeking a Senior Platform Engineer for its Platform-as-a-Service Engineering team, which builds the foundational platform that enables product teams to deliver software safely and quickly. The team’s current stack includes AWS, EKS, Argo CD, GitHub Actions, and Cortex, and is moving toward AI-augmented orchestration and intelligent automation. The engineer will design, build, and operate agentic systems and core infrastructure, creating automation that reduces operational toil and improves developer self-service across EarnIn. The role involves independently addressing complex, well-scoped problems, re-architecting components, adopting technologies, and improving processes with minimal guidance, while aligning with Staff engineers and the manager on technical direction. This is a hybrid position based in Mountain View, with in-office work required two days per week. The base salary range is $228,000–$279,000, plus equity and benefits. Applicants must be authorized to work in the United States. EarnIn can support H-1B and O-1 employer transfers, but cannot provide new visa sponsorship or consider candidates working under OPT or CPT.

Requirements

Bachelor’s or master’s degree in Computer Science, Engineering, or a related field.
At least 4 years of experience in cloud infrastructure, working on large-scale, highly available, customer-facing distributed systems.
Experience mentoring junior engineers and leading well-scoped platform initiatives within a team.
Hands-on experience building and operating production AI-driven systems, including agentic workflows that perform operational tasks.
Experience reducing operational toil through agentic AI on at least one meaningful workflow.
Strong working knowledge of AWS, including services such as EKS, Lambda, and Bedrock, and experience with containerized and serverless architectures.
Strong expertise in Kubernetes at scale and ability to implement complex, resilient solutions with guidance from senior or staff engineers.
Strong knowledge of infrastructure-as-code tools such as Terraform and Ansible, including experience with traditional IaC and agentic automation.
Strong command of Datadog and observability practices, using metrics to inform decisions.
Adherence to security, privacy, and compliance best practices, with awareness of production AI governance such as model safety, prompt injection prevention, and data isolation.
Experience with LLM orchestration frameworks such as LangChain, LlamaIndex, CrewAI, or custom agentic architectures, and production prompt engineering.
Strong coding ability in Python and/or Go, and experience treating infrastructure and agentic systems as software.
Ability to collaborate effectively across engineering, product, and security teams.
Current authorization to work in the United States; new visa sponsorship is unavailable, and candidates working under OPT or CPT are not considered.

Nice To Haves

Experience building AI-driven alerting or root-cause analysis systems.
Experience with service mesh technologies such as Linkerd or Istio and traffic management at scale.
Proficiency with GitOps tools such as Argo CD or Flux CD and CI/CD orchestration tools such as GitHub Actions or Argo Workflows.
Experience with MLOps or LLMOps concepts, including model versioning, evaluation frameworks, and production monitoring for AI systems.
Familiarity with security frameworks for AI systems, including guardrails, audit logging, and data governance for LLMs.

Responsibilities

Design, build, and operate production AI agents in accordance with governance practices for model selection, evaluation, and safety.
Version, review, and test prompts, tools, and evaluation criteria as infrastructure-as-code components.
Implement agentic patterns for cloud infrastructure and contribute to team best practices for production AI agents.
Support and mentor less experienced engineers on agentic patterns, LLM integration, and prompt engineering.
Operate and improve highly available distributed systems on AWS, identifying and resolving performance, scalability, and stability issues within the engineer’s domain.
Use AI-driven observability and anomaly detection to identify problems early.
Evolve infrastructure-as-code and automation standards for owned services, incorporating agentic pattern recognition and automated remediation where appropriate.
Develop the Cortex developer control plane as an AI-augmented self-service platform, including features for service scaffolding, deployment debugging, and issue resolution through natural language.
Encode platform standards and security policies into AI-powered golden paths.
Document agentic architecture, best practices, and operational procedures.
Participate in on-call rotations and use post-mortems to improve reliability and agentic automation.
Collaborate across engineering, product, and security teams on cross-functional work.