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Posted 3 days agoApplied AI Engineer
at HackerRank
IndiaHybrid
Responsibilities
- Own agents end-to-end after launch: monitor quality, triage failures, review outputs, and iterate based on real usage data.
- Design and build the shared AI Agents platform: orchestration, routing, reusable components, access controls, logging, rate limiting.
- Build evaluation harnesses that measure agent quality programmatically, catch regressions before they reach users, and give the team confidence to ship changes.
- Author and maintain MCP (Model Context Protocol) connectors that give AI agents access to HackerRank's internal tools, APIs, and data systems.
- Own the outputs you ship. Write and review code with the same rigor you would apply to any production system - secure, well-tested, and maintainable. Who you are
Requirements
- Software has entered an era where humans and AI build side by side.
- You will be part of a team whose job is to make every team at HackerRank - from Go-To-Market to Finance to Product - dramatically more effective, by building AI agents, automations, and platform capabilities that eliminate the work that should not be manual in the first place.
- In a given month you might be building a data analytics agent that answers ad-hoc product questions in Slack, writing an MCP connector that gives AI agents access to internal tooling, or designing the eval harness that lets the team ship agent changes without regressions. What you will do
- Embed with internal teams - from Marketing to Product to Finance - to map workflows and identify the real bottlenecks which can be impacted by AI.
- Scope, prototype, and ship AI agents and automations that eliminate high-leverage manual work across the company.
- experience with a track record of shipping production systems, not just demos or prototypes.
- Strong Python skills and solid fundamentals across the stack - you can build and deploy a service, wire it to APIs and databases, and keep it running. Hands-on
- experience building with LLMs (Anthropic, OpenAI, or similar) in production, including prompt engineering, context management, tool use, and debugging failure modes at real scale.
- You have built or operated structured evaluation pipelines for AI systems with metrics and regression detection.
- Fluency with AI tools and agents - not just as a user, but as someone who builds production systems on top of them and understands their failure modes deeply enough to debug and improve them.