AI Backend Engineer (SDE-2)

Overview

Join our rapidly growing AI startup as an AI Backend Engineer and build the agentic core of our platform: the LLM workflows, agent orchestration, and AI-driven systems that turn natural-language marketer intent into live, personalized in-app experiences at scale. This role is for an engineer who pairs strong backend fundamentals with hands-on applied AI, and who wants to own systems end to end on a high-throughput production hot path.

Plotline is backed by Elevation Capital and supports customers like Dream11, Upstox, and Zepto.

Why This Role Matters

Build the AI layer that turns marketer intent into live in-app experiences for 300M+ end users.

Work directly with the engineering team and founders to shape the agentic core of the product.

Design backend systems and patterns that scale with our high-growth trajectory.

We're seeking a rare combination of backend rigor, applied AI judgment, hands-on execution, and the drive to own systems end to end.

About This Role

Key challenges you'll solve:

Agent Orchestration: Building LLM agents that plan multi-step and use tools reliably, deciding which steps to take rather than following a hardcoded script.

Production Reliability: Keeping a non-deterministic AI system fast, safe to retry, and observable on a sub-100ms hot path serving millions of devices.

This is an opportunity to have outsized impact on the core of the product while building deep expertise in agentic systems at a scaling startup.

Outcomes Expected

Own and scale both our core backend and the agentic core, with the data modeling, resilience, and cost controls needed to run them reliably in production at scale.

Ship grounded, tool-calling agent flows end to end, backed by retrieval and a real evaluation harness.

Responsibilities

Agent & AI Systems

LLM Orchestration: Design agents with tool-calling and real multi-step planning that select tools and recover when a tool fails or returns nothing useful.

Retrieval (RAG): Build chunking, embedding, and retrieval pipelines that ground the agent's outputs in real data and best practices.

Evaluation & Guardrails: Build the evals and safeguards needed to measure and trust output quality in a non-deterministic system.

Backend & Infrastructure

Production Services: Own backend services on a high-throughput, low-latency hot path, with idempotency, resilience, and clean failure handling.

Data Modeling: Model and query behavioral and profile data to power segmentation, personalization, and campaign logic.

Latency & Cost: Keep the system fast and economical, avoiding unnecessary model calls and managing budgets as it scales.

Requirements

Professional Experience

2 to 4 years of overall engineering experience, including 1+ years building AI/LLM systems, with a track record of shipping production systems under real load.

Hands-on experience building with LLMs (tool-calling, agent orchestration, retrieval, or prompt and context design).

Comfort with the operational side: timeouts, retries, fallbacks, tracing, and debugging a bad run.

Technical Proficiency

Backend Languages: Proficiency in one or more of Go, Python, or TypeScript/Node. We care more about how you reason than which stack you have used.

AI Tooling: Familiarity with LLM APIs, vector search, embeddings, and chunking strategies.

Systems Fundamentals: Strong data modeling, concurrency, and API design, with a sharp instinct for safe retries and observability.

Core Competencies

Engineering Judgment

Systems Thinking: Designs for scale, reliability, and clear failure modes from the start.

Quality Standards: Values clear judgment and craft over feature count, and can explain and defend tradeoffs.

Technical Execution

Self-Sufficiency: Ships complete systems end to end, from data model to deployed service.

Applied AI Sense: Knows when to use an LLM, when not to, and how to keep outputs grounded and trustworthy.

Startup Agility

Rapid Iteration: Balances rigor with speed of execution and learning.

Growth Mindset: Continuously evolves the system and approach based on production signals and feedback.

Ready to build the AI core of a fast-growing platform? If you're excited by the intersection of solid backend engineering and applied AI, we'd love to hear from you.