Build agent-driven video generation pipelines using LLMs, generative models, and backend systems at scale for celebrity ad creation.

About the Company

TrueFan AI is a generative-AI platform building AI-powered celebrity video ads and hyper-personalized marketing solutions for Indian brands. Operating at the intersection of GenAI, video, voice, and automation, the company develops production systems that create content at scale. TrueFan AI is committed to pushing the boundaries of what AI can achieve in the media and advertising space, enabling brands to deliver unique, engaging experiences to their audiences.

Role Overview

We are looking for an AI Engineer experienced in building large-scale, agent-driven video generation pipelines. In this role, you will build systems where AI agents orchestrate multiple models and tools to take a video brief from idea → script → scenes → generation → voice/lip-sync → editing → validation → final video. This position sits at the intersection of AI agents, generative video, backend engineering, and distributed systems. The ideal candidate is a hands-on builder who can take experimental AI capabilities and turn them into reliable, scalable production systems.

Key Responsibilities

Agentic Video Generation

• Build multi-step AI agents that autonomously plan and execute video-generation workflows.
• Orchestrate LLMs, image/video generation models, TTS, voice, lip-sync, avatars, and other AI tools.
• Design agent state, tool calling, planning, validation, retries, fallbacks, and human-in-the-loop workflows.
• Build systems that can dynamically decide the next step based on intermediate outputs.

Scale & Infrastructure

• Build high-throughput video-generation pipelines capable of processing large batches and concurrent jobs.
• Design asynchronous workflows using queues, workers, APIs, and distributed systems.
• Optimize pipelines for quality, latency, throughput, reliability, and cost.
• Build robust handling for failures, retries, timeouts, partial outputs, and long-running jobs.

AI & Video Engineering

• Integrate and evaluate new generative video, image, audio, and multimodal models.
• Build automated video-processing workflows using tools such as FFmpeg.
• Develop pipelines for compositing, rendering, subtitles, audio/video synchronization, and asset management.
• Build evaluation and quality-control systems for generated content.

Production Engineering

• Build production-grade Python services and APIs.
• Implement monitoring, logging, tracing, error handling, and automated validation.
• Debug issues across the entire AI pipeline, from agent decisions to model failures and rendering jobs.
• Take systems from prototype → production → scale.

Tech Stack

• AI / Agents: LLM APIs, Agent Frameworks (LangGraph, LangChain, CrewAI), Multimodal AI
• Generative Media: Video, Image, Voice, TTS, Lip-sync
• Backend: Python, FastAPI
• Video Processing: FFmpeg
• Orchestration: Celery, RabbitMQ, Redis, Background Workers
• Infrastructure: Docker, Cloud / GPU Workloads
• Database: MySQL / PostgreSQL

2–4 years of experience in AI/ML, software engineering, or generative AI. Hands-on experience building video-generation or generative-media pipelines. Strong Python and backend engineering fundamentals. Experience building LLM-powered agents and multi-step workflows. Experience integrating multiple AI models/APIs into production pipelines. Strong understanding of asynchronous processing, queues, workers, and distributed systems. Ability to build reliable systems at scale with a focus on latency, cost, and throughput. Strong debugging, problem-solving, and ownership mindset. Nice to have: Experience with LangGraph, LangChain, CrewAI, or similar agent frameworks. Experience with video-generation models, TTS, voice cloning, lip-sync, avatars, or multimodal AI. Experience with FFmpeg, GPU inference, or distributed GPU workloads. Experience with Celery, RabbitMQ, Redis, Kafka, Docker, or cloud infrastructure. Experience building evaluation systems for generative AI.

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