Skip to content
EarnIn

Senior Platform Engineer (AI Focused)

Bengaluru, India; Bengaluru/Bangalore, IndiaIndia
Senior · Engineering
POSTED 13 MAYVERIFIED 14M AGO
Apply Ask
Pay · H-1B filings
$190k
median · n=5 · FY26
Open for
174d
since 13 May
H-1B · FY26
14
certified LCAs
Hiring · 30d
35
+1 net · open roles
KubernetesLLMs

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 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.

WHAT YOU'LL DO

  • 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.
Source · EarnInFirst seen 2 Nov4 editsOriginal ↗