EarnIn: Senior Platform Engineer (AI Focused) — Bengaluru, India

About EarnIn

EarnIn provides an earnings-access application. Workers can receive pay for hours they have already worked before the scheduled payday, with amounts drawn against accumulated earnings and no mandatory fees charged to users; an employer-side programme delivers the benefit without payroll integration. The company is headquartered in Palo Alto, California. Its employer feed lists engineering, operations and support roles for this consumer earnings product.

The position and its setting

EarnIn lists Senior Platform Engineer (AI Focused) in Bengaluru, India, under reference 7895708. The details below separate the work described by the employer from essential conditions, preferred experience and practical preparation advice.

• Department: Engineering
• Employer listing date: 13 May 2026

Responsibilities and work described

• Design: and build MCP servers that expose platform capabilities as safe, well-scoped tools for AI agents and developer-facing assistants
• Contribution: 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
• Implementation: 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 AI workflows

Essential requirements and candidate background

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field, or equivalent practical experience
• Coding skills in Python and/or Go, with experience building and operating production services
• 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
• Cloud cost attribution, workload optimization, and AI/LLM spend control
• Familiarity: 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 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

Experience and professional background

• 4+ years in platform, infrastructure, or backend engineering with hands-on experience operating production systems in a cloud environment (AWS preferred)

Preferred experience and additional strengths

• Deep experience with Kubernetes (EKS preferred), GitOps (Argo CD), CI/CD (GitHub Actions), and Terraform/Helm/Kustomize for infrastructure automation, service mesh

Location, schedule and contract conditions

• Advertised location: Bengaluru, India

Conditions requiring the complete notice

Some passages contain complex or combined conditions that have not been reduced to short eligibility statements here. Read the complete linked notice for those passages, including any alternatives, exceptions or restrictions. The concise facts above should not be treated as the full wording of every condition.

Pay and application dates

A verified salary figure is not established in this article. An online estimate for another Senior Platform Engineer (AI Focused) vacancy is not an offer for reference 7895708; use the employer’s own pay disclosure and recruitment communication.

A closing date is not confirmed in this article. Check the current employer page before submitting: an undated posting can change or be withdrawn.

How to apply to EarnIn

Applications follow the official employer page linked below.

Open the official EarnIn posting below and match the title and reference 7895708.

Compare your qualifications and experience with the essential conditions for the Bengaluru, India position, then read the preferred criteria separately.

Prepare an accurate CV and the supporting items requested in that employer’s application. Provide only information and documents you are authorised to share.

Use the employer’s application option, complete the required fields and review the submission. Retain the acknowledgement when one is provided. Job Alerts World does not receive the application.

Practical preparation: Python

Python work is easier to assess through a concrete example than a list of libraries. Explain the problem, the input data, the parts you implemented and how you checked the output. Include a repository or a short technical note when you can share it.

Relevant skills

• Python
• Kubernetes
• GitOps
• CI/CD
• AWS
• MCP
• AI/ML
• LLMs
• Terraform
• AI
• communication