5 months ago
Bengaluru, IndiaSenior
Responsibilities
- Design and build MCP servers that expose platform capabilities as safe, scoped tools for AI agents and developer assistants.
- Develop agentic platform-operations workflows for incident triage, deployment validation, configuration remediation, and capacity planning.
- Drive AI-assisted code review and developer tooling with tool use, validation steps, and human-in-the-loop gates.
- Operationalize LLM features through structured prompting, RAG, output validation, evaluation harnesses, and LLM gateway/router patterns.
- Design and evolve GitOps continuous delivery and Kubernetes infrastructure on AWS EKS using Terraform, Helm, and Kustomize.
- Improve observability, reliability, SLOs, error budgets, MTTD, and MTTR through automation and operational practices.
- Extend the developer control plane with paved paths, scorecards, and self-service actions.
- Build FinOps automation for compute, observability, and AI/LLM costs to identify waste and support cost-aware decisions.
- Define success metrics and run time-bound experiments measuring developer efficiency, reliability, and cost impact.
- Document usage guidance, patterns, and best practices for adopting proven AI workflows.
Requirements
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
- At least 4 years of platform, infrastructure, or backend engineering experience operating production systems in a cloud environment, preferably AWS.
- 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.
- Solid observability experience with Datadog APM, metrics, tracing, and logs, including reliability improvements and SLO/error-budget practices.
- Hands-on experience building or integrating MCP servers, including tool-surface design, scope and authorization management, and infrastructure integration.
- 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 partner teams in a distributed environment.
- Experience using AI-assisted development tools such as GitHub Copilot, Cursor, or ChatGPT.
Benefits
- Hybrid work arrangement in the Bengaluru office with 2 days on-site
- Healthcare benefits
- Internet and cell phone reimbursement
- Learning and development stipend
- Potential opportunities to travel to the Mountain View headquarters
- Visa sponsorship and immigration support are not provided
Tech Stack
Categories
About EarnIn
EarnIn builds a mobile app for earned wage access and related financial tools for U.S. workers living paycheck to paycheck. It lets users access a portion of accrued wages before payday, plus features like credit monitoring, automated savings, low-balance alerts, and a debit card, with no mandatory fees or interest. Founded in 2012 and headquartered in Mountain View, California, the privately held company issues certain banking products via Evolve Bank & Trust.