# Senior Platform Engineer

**Company:** [Earnin](https://hotfix.jobs/companies/earnin)
**Location:** Bengaluru, India
**Role:** ML Engineering
**Experience:** 5+ years
**Skills:** Python, Go, Kubernetes, Aws Eks, Argo Cd, GitHub Actions, Terraform, Helm, Kustomize, Datadog, Mcp Servers, RAG, Llm Gateways, Finops, GitOps
**Posted:** 2026-07-08

> Build and operate AI-assisted platform infrastructure, developer tooling, and production workflows across Kubernetes, AWS, GitOps, observability, and FinOps. Requires 4+ years of platform, infrastructure, or backend engineering experience and hands-on experience with MCP servers and agentic AI systems.

## Job Description

## Responsibilities
- 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 incident triage, deployment validation, configuration remediation, and capacity planning.
- Drive AI-assisted code review and developer tooling using tool-use, validation steps, and human-in-the-loop gates.
- Operationalize LLM-based features with structured prompting, RAG, output validation, and evaluation harnesses.
- Implement LLM gateway and router patterns to control costs, route models, and provide observability for AI workloads.
- Design and evolve GitOps-based continuous delivery and Kubernetes infrastructure on AWS EKS.
- Manage infrastructure as code with Terraform, Helm, and Kustomize.
- Strengthen observability, reliability, and operational excellence through SLOs, error budgets, metrics, traces, logs, and automation that improves MTTD and MTTR.
- Extend the developer control plane with paved paths, scorecards, and self-service actions.
- Instrument platform cost signals, including compute, observability, and AI/LLM spend, and build FinOps automation.
- Define success metrics and run time-bound experiments evaluating developer efficiency, reliability, and cost.
- Document usage guidance, patterns, and best practices for proven AI workflows.

## Requirements
- Bachelor's or master's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
- 4+ years of platform, infrastructure, or backend engineering experience operating production systems in a cloud environment; AWS preferred.
- Strong Python and/or Go coding skills with experience building and operating production services.
- Deep experience with Kubernetes, preferably EKS; GitOps with Argo CD; CI/CD with GitHub Actions; and Terraform, Helm, and Kustomize.
- Experience with service mesh technologies.
- Strong observability skills with Datadog APM, metrics, tracing, and logs, plus a record of improving reliability and promoting SLO/error-budget practices.
- Hands-on experience building or integrating MCP servers, including tool-surface design, scope and authorization management, and connecting AI agents to infrastructure.
- Production experience with structured or agentic AI workflows, including planning/execution separation, human-in-the-loop validation, RAG, and tool-use patterns.
- FinOps experience covering cloud cost attribution, workload optimization, and AI/LLM spend control.
- Familiarity with LLM gateway and router patterns for cost control, model routing, and AI workload observability.
- Clear communication and effective collaboration with distributed partner teams.
- Experience using AI-assisted development tools such as GitHub Copilot, Cursor, or ChatGPT.

## Benefits
- Healthcare coverage.
- Internet and cell phone reimbursement.
- Learning and development stipend.
- Potential opportunities to travel to the Mountain View headquarters.
- Hybrid work based in the Bengaluru office, with two days on-site.
- Visa sponsorship and immigration support are not available.

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