EarnIn
From EarnIn's careers page
Status: listed on the employer's own careers page when we last checked (daily). Confirm on their site before applying.
As a company, we are becoming AI-native, and we encourage you to use AI tools throughout much of your interview process.
As one of the first pioneers of earned wage access, our passion at EarnIn is building products that deliver real-time financial flexibility for people 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 excited to bring on world-class talent to help shape the next chapter of our growth journey.
EarnIn is becoming an AI-native company, embedding AI agents into how we build, analyze, and operate across every function. Teams use AI Agents to move from ideas to execution faster. By letting agents handle repetitive tasks, our people can focus on judgment, creativity, and hard problems. We pair this experimentation with the strict review and security guardrails our regulated financial environment requires, always keeping a focus on privacy and the people who rely on our products.
As a company, we are becoming AI-native, and we encourage you to use AI tools throughout much of your interview process. If you use AI during the live interview, just say so; a quick, natural mention of how it helped and why you are using it is all we need. What we're evaluating is your judgment, reasoning, and ability to own and defend your work, not whether AI was involved. Some rounds, particularly those focused on your career history and past experiences, will not allow the use of AI, and we'll let you know of those in advance. When in doubt, ask your recruiter or interviewer directly.
POSITION SUMMARY
As a Senior Platform Engineer (AI Focussed), you will design, build, and operate AI-assisted capabilities within platform-owned infrastructure and developer tooling. You will work across Kubernetes, CI/CD, GitOps, observability, FinOps, and infrastructure-as-code to embed AI directly into the systems that power software delivery — from MCP servers, LLM Gateways, and agentic workflows to automated diagnostics and cost optimization.
You will join the Platform Engineering organization. The team’s mission is to provide reliable, scalable tooling that helps engineers build, test, deploy, and operate software efficiently. In this role, you will split your time between standard platform engineering and AI engineering, with a focus on shipping production-grade AI-assisted workflows inside existing systems. This is a hands-on engineering role. Success is measured by real adoption and measurable improvements in developer efficiency, platform reliability, and operational cost.
This position will be a hybrid role based in our Bengaluru office, with 2 days on-site as part of our expanding site. EarnIn provides excellent benefits for our employees, including healthcare, internet/cell phone reimbursement, a learning and development stipend, and potential opportunities to travel to our Mountain View HQ. Our salary ranges are determined by role, level, and location. We are unable to provide visa sponsorship or immigration support for this position.
Design and build MCP servers that expose platform capabilities as safe, well-scoped tools for AI agents and developer-facing assistants. Contribute to enterprise MCP patterns, LLM tooling, agentic guardrails, knowledge repositories, and framework rollout.
Design structured, agentic workflows for platform operations — incident triage, deployment validation, config remediation, capacity planning — and drive AI-assisted code review and developer tooling (e.g., CodeRabbit, Claude/Cursor) with tool-use, validation steps, and human-in-the-loop gates.
Operationalize LLM-based features inside platform tooling: structured prompting, RAG, output validation, and evaluation harnesses. Implement LLM gateway and router patterns to control costs, route models, and enable observability of AI workloads.
Design and evolve GitOps-based continuous delivery, Kubernetes infrastructure on AWS EKS, and infrastructure-as-code with Terraform, Helm, and Kustomize.
Strengthen observability, reliability, and operational excellence: SLOs, error budgets, metrics/traces/logs, and automation that improves MTTD/MTTR.
Extend the developer control plane with paved paths, scorecards, and self-service actions.
Instrument platform cost signals — compute, observability, and AI/LLM spend — and build FinOps automation that surfaces waste and supports cost-aware engineering decisions.
Define success metrics upfront and run time-bound experiments to evaluate impact on developer efficiency, reliability, and cost.
Document usage guidance, patterns, and best practices to support consistent adoption of proven AI workflows.
Bachelor's or Master's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
4+ years in platform, infrastructure, or backend engineering with hands-on experience operating production systems in a cloud environment (AWS preferred).
Strong coding skills in Python and/or Go, with experience building and operating production services.
Deep experience with Kubernetes (EKS preferred), GitOps (Argo CD), CI/CD (GitHub Actions), and Terraform/Helm/Kustomize for infrastructure automation, service mesh..
Solid observability skills (Datadog APM/metrics/tracing/logs) with a track record of improving reliability and driving SLO/error-budget culture.
Hands-on experience building or integrating MCP servers — designing tool surfaces, managing scope and auth, and connecting AI agents to real infrastructure.
Practical experience with structured or agentic AI workflows (e.g., planning/execution separation, human-in-the-loop validation, RAG, tool-use patterns) used in production environments.
FinOps experience: cloud cost attribution, workload optimization, and AI/LLM spend control.
Familiarity with LLM gateway/router patterns for cost control, model routing, and observability of AI workloads.
Clear communication skills and the ability to collaborate effectively with partner teams in a distributed environment.
Experience using AI-assisted development tools (e.g., GitHub Copilot, Cursor, ChatGPT, or similar tools) as part of your software development workflow
#LI-Hybrid
At EarnIn, we believe that the best way to build a financial system that works for everyday people is by hiring a team that represents our diverse community. Our team is diverse not only in background and experience but also in perspective. We celebrate our diversity and strive to create a culture of belonging. EarnIn does not unlawfully discriminate based on race, color, religion, sex (including pregnancy, childbirth, breastfeeding, or related medical conditions), gender identity, gender expression, national origin, ancestry, citizenship, age, physical or mental disability, legally protected medical condition, family care status, military or veteran status, marital status, registered domestic partner status, sexual orientation, genetic information, or any other basis protected by local, state, or federal laws. EarnIn is an E-Verify participant.
EarnIn does not accept unsolicited resumes from individual recruiters or third-party recruiting agencies in response to job postings. No fee will be paid to third parties who submit unsolicited candidates directly to our hiring managers or HR team.
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