Senior AI Engineer (Platform Engineering)

As a Senior AI Platform Engineer, 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 and LLM Gateways and agentic workflows and 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. We are unable to provide visa sponsorship or immigration support for this position.

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 platform operations incident triage, deployment validation, config remediation, and 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.

Requirements:

• 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 and 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.