AI Agent Engineer

PingAura AI Technologies Private Limited.; Mumbai; On-site; INR 1,200,000 - 2,000,000; Full time

Job summary

PingAura AI Technologies Private Limited is looking for an AI Agent Engineer to design and develop AI agents. This unique role includes responsibilities such as improving agent performance and building evaluation benchmarks. You will directly influence our flagship product in a growing startup environment backed by notable investors.

Ideal candidates have substantial experience with large language models. A deep understanding of AI tools and programming in Python or TypeScript is essential. We offer a competitive salary and an opportunity to shape the company culture.

Qualifications

• 2+ years of experience building with large language models.
• Experience in shipping agentic systems that real users depend on.
• Ability to explain AI agent differences from chatbots.

Responsibilities

• Design and iterate agent behaviors for long-horizon workflows.
• Own the Coworker's reasoning loop and structured outputs.
• Build the eval bench to measure quality and latency.

Skills

Production experience with large language models

Agentic systems and prompt chains

Understanding of tokenization and context windows

Clean, testable Python or TypeScript

Tools

Langfuse

OpenAI Evals

React

PostgreSQL

Job description

Build production AI agents at PingAura — the AI Coworker that helps brands win in AI search through long-horizon reasoning, tool use, and real evals.

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 matter more than test coverage. 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.

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, 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 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 Python or TypeScript
• You think about cost per call as much as you think about accuracy
• You have dealt with the hard parts: hallucinations, latency spikes, provider outages, and meaningful evals
• You 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
• 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; Python for ML and eval tooling
• 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.