AI Agent Engineer

PingAura AI; Mumbai; On-site; INR 2,500,000 - 5,000,000; Full time

Benefits offered by this job

Competitive salary

Early team

Impactful work

Job summary

PingAura AI is hiring its first dedicated AI Agent Engineer to own the reasoning layer for the Coworker product. You will design agent loops, tool surfaces, the eval bench, and model-routing logic across OpenAI, Gemini, and Anthropic.

The role demands hands-on experience shipping real AI systems and building reliable production workflows with guardrails, observability, and cost control. The position offers an opportunity to shape the next phase of our AI agent stack and work with a fast-moving

Qualifications

• 2+ years of production experience building with large language models.
• Shipped agentic systems or RAG pipelines that real users depend on.
• Can design, build, debug, and maintain backend APIs and database-backed systems that support LLM workflows.
• Can explain in one sentence why an AI agent is not a chatbot.
• Deep understanding of tokenization, context windows, structured outputs, tool use, and multi-model tradeoffs.

Responsibilities

• Design and iterate on agent behaviors across real-world AEO tasks and long-horizon workflows.
• Own the Coworker's reasoning loop, tool calls, and structured outputs across OpenAI, Google Gemini, and Anthropic.
• Build the eval bench. Measure quality, latency, cost, and regression across model versions.
• Improve agent performance through prompting, tool-use strategies, context construction, and model-facing experimentation.
• Design and build multi-model orchestration - routing, fallbacks, and cost optimization across providers.
• Build guardrails: input validation, output filtering, hallucination detection, and rate management.
• Instrument agent pipelines with observability - tracing, token tracking, and latency monitoring on Langfuse.
• Turn improvements into measurable gains in solve rate, usefulness, and economic value for customers.

Skills

LLM experience

TypeScript

OpenAI integration

AI tooling

Tools

PostgreSQL

Redis

Supabase

Langfuse

OpenAI

Claude

Job description

About The Role

Every day, millions of people skip Google and ask an AI for answers instead. The AI picks a handful of brands to recommend. Most companies have no idea if they made the list. PingAura fixes that - our flagship product is the AI Coworker, an agent that does the optimization work for them.

Building real AI agents is a different discipline from building software. Agents are nondeterministic. They reason in steps. They call tools. They fail in interesting ways. Evals become first-class production infrastructure because normal test coverage is not enough. The model layer changes every quarter, and the architecture has to absorb that without breaking.

We are hiring our first dedicated AI Agent Engineer to own this layer. You will design the agent loops, the tool surfaces, the eval bench, and the model-routing logic behind the Coworker.

We are pre-seed and backed by 14 CXO angel investors, Google for Startups, and AWS. The product is live. The customers are paying.

This is not a demo-builder role. It is for someone who has shipped real AI systems, understands how agent behavior fails under load and ambiguity, and can turn those lessons into evals, tooling, and reliable production workflows.

Responsibilities

• Design and iterate on agent behaviors across real-world AEO tasks and long-horizon workflows
• Own the Coworker's reasoning loop, tool calls, and structured outputs across OpenAI, Google Gemini, and Anthropic
• Build the eval bench. Measure quality, latency, cost, and regression across model versions. Make evals the gate on shipping
• Improve agent performance through prompting, tool-use strategies, context construction, and model-facing experimentation
• Design and build multi-model orchestration - routing, fallbacks, and cost optimization across providers
• Build guardrails: input validation, output filtering, hallucination detection, and rate management
• Instrument agent pipelines with observability - tracing, token tracking, and latency monitoring on Langfuse
• Turn improvements into measurable gains in solve rate, usefulness, and economic value for customers

You may be a good fit if

• You have 2+ years of production experience building with large language models - not demos or hackathon projects
• You have shipped agentic systems, prompt chains, or RAG pipelines that real users depend on
• You can design, build, debug, and maintain backend APIs and database-backed systems that support LLM workflows
• You can explain in one sentence why an AI agent is not a chatbot
• You have a deep understanding of tokenization, context windows, structured outputs, tool use, and multi-model tradeoffs
• You write clean, testable TypeScript and can move through a modern JavaScript stack
• 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 think about cost per call as much as you think about accuracyYou have dealt with the hard parts: hallucinations, latency spikes, provider outages, and meaningful evalsYou have built an eval bench before, or have felt the pain of not having one

Strong candidates may also have

• Experience with eval frameworks: Langfuse, Braintrust, RAGAS, DeepEval, OpenAI Evals, or comparable
• Experience designing reasoning systems for multi-step, tool-using agents
• Working knowledge of Python for eval scripts, automation, or AI tooling
• Experience with PostgreSQL, Redis, Supabase, or similar infrastructure
• Background in search, content, or marketing technology
• 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 - you're joining at the ground floor
• Early team means you help shape the culture, the stack, and the way we work

Why this team

You own the AI systems, not inherit them. There is no legacy architecture - you define it. The product is live and the customers are paying.