ML Engineer Job in Hyderabad (Hybrid)

HinduTone is hiring. This is a current opening at HinduTone, posted on 17 October 2026.

Job details

Employer: HinduTone
Role: Machine Learning Engineer
Location: Hyderabad, Telangana, India
Work mode: Hybrid
Job type: Full-time
Experience: Experience training, evaluating and deploying ML models
Last date: 30 November 2026
Apply: Call / WhatsApp +91-9666019191 or the HinduTone contact page

About the role

Every day HinduTone serves readers looking up panchang timings, festival dates, temple details and careers information in three languages. Behind that traffic sits a lot of data we have barely used: which pages people read together, which topics peak before which festivals, and how readers move between our sections. We are hiring a Machine Learning Engineer in Hyderabad to turn that data into models that make the HinduTone website more useful.

You will own the path from raw data to a model in production: building pipelines, engineering features, training and evaluating models, deploying them and watching them for drift. Typical problems include classifying articles, ranking content for readers, recommending related pages and forecasting traffic around festival seasons. The position is full-time and hybrid, combining time at our Hyderabad office with work from home.

This role suits an engineer who has trained, evaluated and deployed ML models before, and who is as interested in data quality and monitoring as in model accuracy.

What you will do

Build and maintain data pipelines that collect page, search and engagement signals in a clean, privacy-respecting way.
Engineer features from content metadata such as language, festival, deity, region and publish date.
Train classification models that label articles by topic and section, helping editors keep the content library organised.
Develop ranking and recommendation models for related articles, temple pages and festival content.
Forecast traffic ahead of major festivals so the editorial and technical teams can plan content and capacity.
Design offline evaluation with sensible baselines, held-out sets and metrics that match the business question.
Package models for deployment, set up batch or real-time inference, and keep latency within agreed limits.
Monitor models in production for data drift, prediction drift and silent failures, with alerts and retraining plans.
Run careful experiments and report results honestly, including when a simple rule beats a model.
Keep training code, datasets and model versions reproducible and well documented.
Work with editors and developers to understand which predictions would actually change a decision.

Skills we are looking for

Must have

Experience training, evaluating and deploying ML models in a real environment.
Strong Python and solid working knowledge of libraries such as pandas, NumPy and scikit-learn.
Good SQL skills and comfort working with messy, real-world data.
Understanding of evaluation metrics for classification, ranking and regression, and when each one misleads.
Experience with at least one approach to model serving, whether batch jobs, an API or scheduled scoring.
Familiarity with version control and writing tests for data and model code.

Good to have

Experience with recommendation systems, learning-to-rank or time-series forecasting.
Exposure to deep learning frameworks such as PyTorch for text or embedding models.
Knowledge of experiment tracking and model registry tools.
Experience with multilingual or Indian-language text data.
Understanding of seasonal patterns, which matter a lot for festival-driven traffic.

Who should apply

You should hold a degree in computer science, IT, statistics, mathematics or a related field, or have equivalent practical skills shown through real projects. More important than the degree is evidence that you have taken at least one model all the way to deployment and kept it working afterwards.

We are a good match for engineers who like small, focused problems with clear outcomes and who are comfortable working closely with non-technical colleagues. You should be able to work in a hybrid pattern, spending part of the week with the team in Hyderabad. If you enjoy explaining a confusion matrix to an editor as much as tuning hyperparameters, we would like to hear from you, even if your past work was in a different industry such as retail, fintech or media.

How you will grow

As the person responsible for machine learning across our content and traffic data, you will set the standards for how we build, evaluate and monitor models. You will choose which problems are worth modelling, decide what good enough looks like for each one, and design the monitoring that keeps models honest after launch.

The data itself is unusual. Festival calendars follow lunar dates, regional traditions vary across states and countries, and readers switch between English, Hindi and Telugu. Working through these patterns will sharpen your feature engineering and forecasting skills in ways that standard datasets rarely do. You will also have a direct line to the people using your outputs, so you will see the effect of your work quickly. Over time, you can help decide which tools and practices the team adopts for experiment tracking, data validation and retraining.

Preparing your application

Describe one model you deployed, covering the data source, features, evaluation metric and how it was served.
Explain how you monitored that model and what you did when performance dropped.
Share a GitHub repository or write-up that shows clean, reproducible training code.
If you have forecasting or recommendation experience, include a short summary of the approach and the results.
Mention the scale you have worked at in terms of rows, features or requests, without overstating it.
Note any experience with text classification or multilingual data.
Add one example where you chose not to use a model, and explain what you used instead and why.

Before the interview

For a broader picture of how employers in India assess candidates, read our overview of machine learning engineer jobs and the skills they demand. Before we meet, revise evaluation metrics for ranking and classification, the basics of time-series forecasting and common ways of detecting drift in production.

How to apply

Share your updated resume with your relevant experience, key skills and links to work you can show, such as GitHub repositories, deployed projects or technical write-ups.

Call / WhatsApp: +91-9666019191
Online: use the HinduTone contact page, choose “Other” as the reason and attach your resume (PDF, up to 5 MB)
Subject line: Application for Machine Learning Engineer – Models and Pipelines

HinduTone never charges a fee for applications, interviews or joining. If anyone asks you for money in HinduTone’s name, do not pay and report it through our contact page.

Frequently asked questions

Can freshers apply for the Machine Learning Engineer role?

This position needs experience training, evaluating and deploying ML models, so it is not a fresher role. If you are a recent graduate who has already deployed a model and monitored it in use, you may still apply and share that project.

What salary does HinduTone offer for this role?

The salary is not published. It is discussed with shortlisted candidates, taking into account their experience and the depth of their deployment work. HinduTone never charges a fee to any applicant at any point.

Is the Machine Learning Engineer job hybrid?

Yes. It is a full-time hybrid role based in Hyderabad, Telangana, which means part of your week is spent working with the team at the Hyderabad office and part of it is spent working from home.

When does the application window close?

Applications close on 30 November 2026. We start reviewing profiles as soon as they come in, so applying early with a clear project summary gives your application the best chance of a careful look.

How can I apply for this position?

Call or WhatsApp +91-9666019191, or send your resume through the HinduTone contact page with the reason set to Other, using the subject line 'Application for Machine Learning Engineer – Models and Pipelines'. Include a link to code or a write-up of a deployed model.

What kinds of models will I work on?

Expect classification models for tagging content, ranking and recommendation models for related pages and temples, and traffic forecasting around festival seasons. You will also build the pipelines and monitoring that keep these models reliable once they are live.

Editor's note (17 October 2026)

This is an official HinduTone job opening. Salary is discussed with shortlisted candidates and is not listed here. If the role is filled or closed, this page will be updated.