Junior AI Engineer
YAL — Hyderabad, Telangana
₹11–18 LPA (HireSetu estimate) · 0–4 yrs exp · Freshers eligible · Full-time · onsite
HireSetu listing reference 6086cc — confirm requirements below, then apply on the employer site.
Quick answer: The 0-4 year band is genuine, and a working LLM project you can walk through counts more than a degree title. The work spans text and voice agents for customer-facing systems, onsite in Hyderabad.
From YAL's job posting
Junior AI Engineer
0-4 years
Full-Time
At YAL, we are building AI agents and conversational systems that help organisations simplify processes and automate everyday work.
We are looking for a Junior AI Engineer who enjoys building practical applications, understanding user problems, and making AI work reliably in real situations. You will work alongside senior engineers to develop agents, integrate them with business systems, and improve them through testing and user feedback.
This role suits someone who wants to understand both how a system works and how people will use it.
What you will do
Build AI agents that understand requests, retrieve relevant information, use tools, and carry out defined tasks.
Develop conversational experiences across text and voice, including conversation flow, context handling, and human handoffs.
Connect agents with APIs, databases, knowledge sources, and internal or customer systems.
Work on prompts, retrieval pipelines, structured outputs, and workflow logic to improve response quality and task completion.
Support speech-to-text and text-to-speech integrations for voice applications.
Participate in requirement discussions with product teams and customer stakeholders, turning practical needs into clear technical tasks.
Take scoped features from prototype through integration, testing, and deployment, with guidance from senior engineers.
Review conversation logs, investigate failures, and improve reliability, response time, and usage costs.
Document integrations, implementation decisions, and setup steps so solutions are easier to maintain and extend.
What we are looking for
Good working knowledge of Python and the ability to write clear, maintainable code.
Familiarity with REST APIs, Git, databases, and basic backend development.
Practical experience building with LLMs through projects, internships, or professional work.
A basic understanding of prompting, retrieval-augmented generation (RAG), and tool calling.
A structured approach to debugging and testing, including checking whether an AI application completes the intended task.
Clear communication and the willingness to ask questions when requirements are incomplete.
Ownership of assigned work, curiosity about user needs, and comfort incorporating feedback.
Good to have
Experience with an agent framework or workflow orchestration tool.
Exposure to conversational AI, speech recognition, speech synthesis, or real-time audio.
Familiarity with Docker, cloud deployment, logging, or application monitoring.
Experience connecting an application to an external service or adapting a project for actual users.
What you will gain
Hands-on experience across AI development and implementation, mentorship from senior engineers, and exposure to how customer requirements shape working products. You will see your work move from an initial problem statement to a usable application.
When applying
Share your resume and, if available, a GitHub repository, demo, or short description of something you have built. Tell us what problem it solved, what you contributed, and one challenge you worked through.
Text from YAL's official job posting, formatted by HireSetu. The employer's own page is the final word on details.
HireSetu's note
The experience that counts here
Someone with six months of hobby building can land this next to a candidate with two years of backend work. The listing asks for practical LLM experience from projects, internships or jobs, so a final-year student with a RAG app that real people used is squarely in range. Python needs to be comfortable at the level where you write the glue around a model: HTTP calls, JSON handling, retries, and code the next person can read.
A career switcher with no shipped code, or only course certificates, will find the gap hard to close, because there is nothing to discuss in the conversation beyond theory. Voice work is the other fork. Speech-to-text and text-to-speech pipelines behave differently from chat, and debugging audio feels unlike debugging text.
The unglamorous parts
Agents fail quietly. The model picks the wrong tool, retrieval hands back a chunk that is nearly right, context drops two turns back, and nothing throws an error. Finding that means opening conversation logs, reproducing the failure, and working out whether the fix sits in the prompt, the retrieval step, or the surrounding code. Expect a good share of your week to go here.
Cost and response time are treated as things to manage, so a fix that adds two more model calls is a tradeoff, not a free win. If you have built anything, know how it was deployed, what data it touched, and where it broke. The stack around all this is unremarkable: Git, REST APIs, a database, containers for shipping.
Drafted with AI from YAL's job posting and checked by Prashanth Jakkula before publishing. See our Editorial Policy.
How to apply
- Open YAL's careers site with the Apply button.
- Follow the application steps on the site and upload your resume.
- Keep the confirmation email or application number for follow-up.
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