About the role

The way software gets built has changed. At PingAura, AI agents write a real share of our code, and humans make the calls that matter - what to ship, what to merge, what to retire. We are hiring an Applied AI Engineer to live at that boundary: dispatch agents, review pull requests, design evaluations, and own the long tail of production engineering that keeps the product reliable.

PingAura helps brands get discovered in AI search. Our flagship product is the AI Coworker - an agent that helps brands improve visibility across AI answer surfaces like ChatGPT, Gemini, and Claude. We are pre-seed and backed by 14 CXO angel investors, Google for Startups, and AWS. The product is live, customers are paying, and decisions move fast.

Experience: 2+ years of professional full-stack engineering experience.

This role is designed for engineers who have already shipped production software and now want to work deeply with AI coding agents, LLM workflows, evals, and production systems. The work goes beyond prompting: you will design, build, review, debug, evaluate, and ship production systems across the stack.

You should be comfortable moving across frontend, backend, APIs, databases, background jobs, observability, and deployment workflows. You do not need to be an AI researcher, but you should already be using tools like Cursor, Claude Code, Codex, or similar AI coding tools seriously in your development workflow.

This is an on-site role at our Mumbai office. You will sit next to the founding team. You will hear the customer calls. You will push code on day one.

Responsibilities

Dispatch and supervise AI-assisted development workflows in parallel - using tools like Cursor, Claude Code, and Codex - across worktrees on real product work
Review agent-generated pull requests with sharp judgment. Catch subtle bugs, missing edge cases, and weak architectural decisions; push back fast when work is not ready
Design evaluations and acceptance criteria so each agent run is faster, sharper, and more autonomous. Turn customer escalations into permanent regression tests
Own the long tail of production engineering: cron jobs, internationalization, dependency upgrades, monitoring, schema housekeeping, and security patches
Ship product features end to end when the work is judgment-heavy and should not be delegated
Improve the internal tooling that makes the agent fleet faster: dispatch scripts, eval harnesses, prompt libraries, agent observability
Join customer calls. Translate what you hear into evals, tests, and shipped code

You may be a good fit if

You have 2+ years of professional full-stack engineering experience
You have independently shipped production features used by real users
You have strong JavaScript/TypeScript experience
You have experience with React/Next.js or similar frontend frameworks
You can design, build, debug, and maintain backend APIs and database-backed systems
You are comfortable moving across frontend, backend, APIs, databases, background jobs, observability, and deployment workflows
You use AI coding tools such as Cursor, Claude Code, Codex, or similar tools seriously in your development workflow
You are comfortable reviewing AI-generated code instead of blindly accepting it
You can quickly review a pull request, identify risks, and explain what needs to change
You are more excited about orchestrating multiple agents in parallel than manually doing repetitive implementation work
You treat evals as first-class production infrastructure, especially for LLM workflows where normal test coverage is not enough
You write clean TypeScript and care about readability, testability, and small interfaces
You have built or contributed to at least one production feature involving LLM calls, tool usage, structured outputs, or AI-assisted workflows
You bring strong debugging, product thinking, and ownership mindset
You are interested in LLM evals, observability, and production reliability
You live in Mumbai or can move here. This is an on-site role

Strong candidates may also have

Working knowledge of Python for eval scripts, automation, or AI tooling
Experience with PostgreSQL, Redis, Supabase, or similar infrastructure
Experience with OpenAI, Anthropic, Gemini, or other LLM providers in production
Experience with tool calling, structured outputs, streaming, or long-horizon LLM workflows
Familiarity with eval and observability tools such as Langfuse, Braintrust, RAGAS, DeepEval, or OpenAI Evals
Experience building internal tools, automation, or agentic workflows
Exposure to GCP or AWS in production
Experience working in fast-moving startup environments
Open-source contributions to AI tooling, agent frameworks, or developer infrastructure

What we work with

Language: TypeScript across the stack
Web: Next.js 16 (App Router), React 19, Server Actions, Tailwind, Shadcn UI
Database: PostgreSQL on Supabase with row-level security; pg_cron and pgmq for scheduled and queued work
Cache and rate limiting: Redis on Memorystore - caching, distributed rate limiting, queue patterns
AI: OpenAI, Gemini, and Anthropic via a multi-provider routing layer
Observability: Langfuse for LLM traces, Sentry for errors, plus standard cloud logging and monitoring
Cloud: GCP for compute, data, and storage; AWS for CDN
Workflow: Turborepo monorepo, pnpm, Cursor and Claude Code daily

Compensation

Competitive salary benchmarked for strong full-stack engineers at early-stage startups in India, with meaningful upside based on ownership, speed, and quality of execution

Why this team

You operate at the new frontier of how software gets built. Most engineers are still learning to use AI tools well; you will help define what comes after that. The team is small, the customers are real, the founders are accessible, and the decisions are fast.

Applied AI Engineer