Machine Learning Engineer Mid · Full-time · Remote →

Thewebvale; Bhopal; Hybride; INR 1 200 000 - 2 500 000; Plein temps

Avantages offerts par ce poste

Remote-first

Bhopal studio access

Direct work with founder & senior team

Portfolio exposure

Résumé du poste

TheWebVale is seeking a Machine Learning Engineer to build predictive models, recommendation systems, and inference APIs for multiple client domains in India. You will work with Python, PyTorch, Scikit-learn, and FastAPI to deploy low-latency services and monitor model performance in production.

You’ll collaborate with backend engineers and data scientists, contribute to experimental design, and help ship features in regular 2–3 week release cycles.

Qualifications

• 5+ years of practical experience developing and deploying machine learning models.
• Strong proficiency in Python, NumPy, Pandas, Scikit-learn, and SQL.
• Hands-on with deep learning frameworks like PyTorch or TensorFlow.
• Foundational statistics, probability distributions, and regression techniques.
• Experience building RESTful model serving APIs with FastAPI or Flask.
• Ability to communicate statistical findings clearly to project leads.

Responsabilités

• Train, evaluate, and fine-tune machine learning models on structured business datasets.
• Build automated data preprocessing and feature engineering pipelines in Python.
• Deploy trained models as low-latency microservice endpoints using FastAPI or Docker.
• Evaluate model performance using cross-validation, precision, recall, and ROC curves.
• Monitor production inference drift and schedule model retraining intervals.
• Collaborate with product designers to present predictive insights clearly to end users.

Connaissances

Python

Scikit-learn

PyTorch

NumPy

FastAPI

SQL

Docker

Description du poste

You will develop predictive machine learning models, statistical analyses, and inference APIs that extract actionable insights from client data.

As a Machine Learning Engineer at TheWebVale, you will construct predictive models, recommendation algorithms, and anomaly detection pipelines. You will analyze business data for retail brands, coaching networks, and property companies to forecast demand, classify records, and uncover operational trends.You will collaborate with backend developers and data engineers within our regular two-to-three week release cycles. You will package models into lightweight API endpoints, write data cleaning scripts, and ensure dependable inference performance in production.

Machine Learning Engineer

• Python
• Scikit-learn
• PyTorch
• NumPy
• FastAPI
• SQL
• Docker

Responsibilities

6 things, one at a time. Keep scrolling.

• Train, evaluate, and fine-tune machine learning models on structured business datasets.
• Build automated data preprocessing and feature engineering pipelines in Python.
• Deploy trained models as low-latency microservice endpoints using FastAPI or Docker.
• Evaluate model performance using cross-validation, precision, recall, and ROC curves.
• Monitor production inference drift and schedule model retraining intervals.
• Collaborate with product designers to present predictive insights clearly to end users.

Requirements

6 things, one at a time. Keep scrolling.

• 01/06 Hold 2-4 years of practical experience developing and deploying machine learning models.
• 02/06 Demonstrate strong proficiency in Python, NumPy, Pandas, Scikit-learn, and SQL.
• 03/06 Show practical familiarity with deep learning frameworks like PyTorch or TensorFlow.
• 04/06 Understand foundational statistics, probability distributions, and regression techniques.
• 05/06 Possess experience building RESTful model serving APIs with FastAPI or Flask.
• 06/06 Communicate statistical findings and model certainty levels clearly to project leads.

What you get.

• Remote-first, with a Bhopal studio for hybrid days
• Real client products across many industries
• Direct work with the founder and senior team
• Learning and tooling support
• Your work featured in our public portfolio

How we hire

1. Intro chat

   A short call about what you want to work on and how you like to work.

2. Work review

   Walk us through projects, code or designs you are proud of.

3. Paid task

   A small paid task or pairing session, so both sides see the fit.