Machine Learning Engineer

Technostacks; Ahmedabad District; On-site; INR 600,000 - 900,000; Full time

Job summary

Technostacks is seeking a Python Machine Learning Engineer with approximately 2 years of hands-on experience in developing, training, evaluating, and deploying machine learning models. The role emphasizes classical ML, data preprocessing, feature engineering, and production-oriented Python development.

Note: MLOps, time-series, and scientific-data experience are preferred but not mandatory. Strong Python stack skills and API exposure are valued for deployment.

Qualifications

• 2 years of hands-on Python and ML experience.
• Strong foundation in classical ML, data preprocessing, feature engineering and model evaluation.
• Experience with Pandas/NumPy; knowledge of at least one end-to-end ML project.

Responsibilities

• Develop and optimize ML models using Python and scikit-learn.
• Preprocess data, perform feature engineering, and evaluate models.
• Deploy models and expose them via APIs (FastAPI/Flask/Django).
• Collaborate on data pipelines and production-ready code.

Skills

Python

Pandas

NumPy

scikit-learn

PyTorch

TensorFlow

CNN

RNN

LSTM

Git

Docker

FastAPI

Flask

Django

PostgreSQL

MySQL

MongoDB

Redis

MLflow

DVC

Tools

Git

Docker

FastAPI

Flask

Django

PostgreSQL

MySQL

MongoDB

Redis

Pandas

NumPy

Job description

Machine Learning Engineer (Python)

Experience: 2 Years | Focus: Machine Learning & Python

Role Overview

We are seeking a Python Machine Learning Engineer with approximately 2 years of hands-on experience in developing, training, evaluating, and deploying machine learning models. The ideal candidate should have a strong foundation in classical machine learning, data preprocessing, feature engineering, statistical analysis, and production-oriented Python development.

Note: MLOps, advanced time-series/signal processing, and scientific-data experience are preferred but not mandatory for this role.

1. Core Machine Learning Development

• Develop and optimize machine learning models using Python and frameworks such as scikit-learn. Basic hands-on experience with deep learning frameworks such as PyTorch or TensorFlow is preferred.
• Apply supervised and unsupervised learning techniques to real-world problems.
• Good understanding of Regression, Classification, and Clustering algorithms. Understand model evaluation techniques such as cross-validation, ROC-AUC, Precision, Recall, and F1-score.
• Perform feature engineering, feature selection, data preprocessing, normalization, and scaling.
• Understand model training, validation, hyperparameter tuning, and performance optimization.
• Ability to analyze model results and identify opportunities for improvement.

2. Data Processing & Analysis

• Strong practical experience with Pandas and NumPy.
• Ability to clean, transform, analyze, and prepare raw datasets for machine learning.
• Experience working with structured datasets and building reusable data-processing workflows.
• Basic understanding of data quality, missing values, outliers, normalization, and feature preparation.
• Polars is optional.
• Experience with time-series data is optional/preferred.

3. Deep Learning & Advanced ML (Preferred)

• Basic practical understanding of neural networks and deep learning concepts.
• Hands-on experience with CNN, RNN, or LSTM is preferred but not mandatory.
• Ability to understand and work with existing deep learning models.
• Experience with time-series modeling, signal processing, peak/event detection, or waveform data is optional.
• Experience with scientific or instrument-generated datasets, including mass spectrometry data, is a plus but not mandatory.

4. Model Deployment & Software Engineering

• Develop clean, modular, and production-ready Python code.
• Basic hands-on experience with FastAPI, Flask, or Django for serving ML models is preferred.
• Understand how to expose trained models through APIs.
• Experience with Git and collaborative software development workflows.
• Ability to write basic unit tests and maintain code quality.
• Basic Docker knowledge is preferred.

5. MLOps (Optional)

• Basic understanding of the machine learning lifecycle.
• Exposure to MLflow, DVC, or similar experiment/model tracking tools is a plus.
• Basic understanding of model monitoring and model drift is a plus.
• Basic understanding of CI/CD pipelines for ML deployment is preferred but not mandatory.
• Experience deploying ML workflows using Docker is a plus.

6. Database & Data Handling

• Basic to intermediate experience with PostgreSQL or MySQL.
• Ability to write queries and efficiently retrieve data required for ML workflows.
• Understanding of data pipelines and ETL concepts.
• MongoDB or Redis experience is optional.

7. Key Requirements Summary

Requirement Category

Description

Experience

2 years of hands-on experience in Python and Machine Learning.

Core ML

Strong foundation in classical Machine Learning, practical model development, data preprocessing, feature engineering, model training, and evaluation.

Technical Stack

Proficiency in Python, Pandas, and NumPy. Practical experience with at least one end-to-end ML project.

Software Engineering

Good understanding of software engineering practices, Git, and clean Python development.

Optional / Plus

Deep Learning, MLOps, time-series modeling, signal processing, and scientific/mass-spectrometry data experience.