Senior Platform Engineer

EarnIn • Mountain View, CA

11d • Hybrid

About The Position

EarnIn’s Platform-as-a-Service Engineering team builds the foundational “Paved Road” that helps product teams ship with velocity and safety. The platform runs on AWS, EKS, and Argo CD, and is evolving toward AI-augmented orchestration that anticipates developer needs and automates operational work. The Senior Platform Engineer will design, build, and operate agentic systems and core infrastructure across Kubernetes, GitOps, CI/CD, and the Cortex developer portal. The work includes building production AI agents for incident triage, environment provisioning, configuration generation, and developer self-service.

The role independently handles well-scoped but complex engineering problems, re-architects components, adopts technologies, and improves processes with minimal guidance. The engineer partners with Staff engineers and their manager on technical direction, mentors less experienced engineers, operates high-availability distributed systems, documents systems and operational practices, and participates in on-call rotations. This is a full-time hybrid position based at EarnIn’s Mountain View headquarters, with in-office work required two days per week. Applicants must already be authorized to work in the United States. EarnIn supports H-1B and O-1 employer transfers, but does not provide new visa sponsorship or consider applicants working pursuant to 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 involving 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; ability to discuss specific agent designs or automation built.
• Experience reducing operational toil with agentic AI on at least one meaningful workflow, such as LLM-powered incident diagnosis, intelligent CI/CD test selection, or self-service assistants.
• Strong working knowledge of AWS, including services such as EKS, Lambda, and Bedrock, and experience with containerized and serverless architectures.
• Solid 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, and experience with both traditional IaC and agentic automation.
• Strong command of Datadog and observability practices, using metrics to drive decisions.
• Strong adherence to security, privacy, and compliance best practices, with awareness of production AI governance, including 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 on cross-functional work.

Nice To Haves

• Experience building AI-driven alerting or root-cause analysis systems.
• Experience with service meshes 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, including model versioning, evaluation frameworks, and production monitoring for AI systems.
• Familiarity with security frameworks relevant to AI systems, including guardrails, audit logging, and data governance for LLMs.

Responsibilities

• Design, build, and operate production AI agents, following governance practices for model selection, evaluation, and safety.
• Apply infrastructure-as-code practices to agentic systems, ensuring prompts, tools, and evaluation criteria are versioned, reviewed, and tested.
• Implement agentic patterns for cloud infrastructure in the engineer’s area of ownership and help refine production AI-agent best practices.
• 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.
• Use AI-driven observability and anomaly detection to identify problems early.
• Evolve infrastructure-as-code and automation standards, including agentic pattern recognition and automated remediation where useful.
• Contribute to Cortex as an AI-augmented self-service developer 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 with Staff engineers and the manager to align work with the team’s technical direction.

Benefits

• Equity
• Benefits

What This Job Offers

• Job Type: Full-time
• Career Level: Senior