AI Engineer
Company: TrueFan AI
Location: Gurgaon, India (on-site)
Function: AI/Product Engineering
About TrueFan AI
TrueFan AI is a generative-AI platform building AI-powered celebrity video ads and hyper-personalized marketing for Indian brands. We sit at the intersection of GenAI, voice, video, and automation shipping production systems that talk, sell, and create at scale.
About the Role
We build a portfolio of autonomous, agentic products across different parts of the business from sales and lead generation to content and localization. We're looking for an AI Engineer who can move fast integrating tools and APIs, wiring up agent pipelines, and shipping working systems quickly. You'll move across products as needed the common thread is always "take a business process that currently needs a human, and turn it into a reliable agent pipeline."This is a junior-to-mid role and we care more about sharp thinking, ownership, and a fast learning curve than years of experience. You'll work closely with the founding team on both the technical build and the underlying process/strategy for whichever product you're on.
What You'll Do
● Work across multiple agent products, each automating a different business process end-to-end.
● Help define the business logic behind each agent what "good" looks like for a given task and translate that into concrete, testable rules the agent can follow.
● Build and maintain data connectors and integrations CRMs, external registries/APIs, publishing or delivery targets, and whatever each product needs to talk to.
● Own workflow and task orchestration background jobs, queues, and webhook callbacks that move something from raw input to finished output without manual intervention.
● Iterate based on real outcomes track versions of configs/prompts, compare results across versions, and help the team understand why a pipeline's output quality moves.
● Debug pipeline issues end-to-end a stuck job in the queue, a malformed payload hitting a downstream API, a job that silently failed halfway through a batch.
● Work with practical LLM tooling using an LLM to turn plain-language criteria into structured queries or outputs, and using agent tools for research/drafting/QA.
What We're Looking For
● 1–3 years of experience, with solid Python fundamentals (can read and write real backend code, not just notebooks).
● Working knowledge of LLMs and how to build with them prompting, structured output, function/tool calling, and agent patterns (multi-step reasoning, tool use, hand-offs).
● Comfortable working with APIs, databases, and background job/queue systems (even if you haven't used our exact stack before).
● Good at quickly integrating third-party tools and APIs, and getting a working pipeline shipped fast rather than over-engineering it.
● Know how to evaluate agent/LLM output setting up evals, comparing prompt/config versions, and catching regressions before they hit production.
● Understanding of RAG how retrieval pipelines are built, chunking/embedding basics, and when RAG is (and isn't) the right tool.
● Understand what it takes to move an agent from "working in a notebook/demo" to running reliably in production error handling, retries, monitoring, and graceful failure.
● Comfortable switching context across different products/pipelines, and comfortable with ambiguity a lot of what we're building doesn't have a playbook yet, so you'll help write it.
● Genuinely willing to learn and put in the work, this is a problem-solver's role more than a "follow the spec" role, and the right person treats an unfamiliar tool or a messy production bug as something to dig into, not hand off.
● Strong communication you'll regularly need to translate between "what the business wants" and "what the system currently does."
Nice to Have
● Exposure to FastAPI, SQLAlchemy, or any Python web backend.
● Familiarity with task queues and workers (Celery, RabbitMQ, or similar).
● Any prior exposure to LangChain/LangGraph or agent frameworks helpful but not required.
● Interest in business process automation, content workflows, or CRM/sales strategy.
● Frontend exposure (React/Next.js) is a plus but not required for this role.
Our Stack (representative, evolving)
Python, FastAPI, SQLAlchemy, Celery + RabbitMQ, LangGraph, MySQL, Next.js/React (frontend team), Docker. LLM usage is via API for structured output generation, content, and QA tasks.
This is a builder's role at a fast-moving AI company and you'll ship things that go live and get used the same week you build them.
Pay: ₹800,000.00 - ₹900,000.00 per year
Benefits:
Ability to commute/relocate:
- Gurugram, Haryana (Gurugram): Reliably commute or planning to relocate before starting work (Required)
Work Location: In person
