Machine Learning Engineer

Data Eminence; India; Remote; INR 900,000 - 1,500,000; Full time

Benefits offered by this job

Fully remote work

Paid time off

Professional development

Company-provided equipment

Job summary

Data Eminence in India is seeking a remote Machine Learning Engineer to build data-driven solutions, preprocess data, train models, and deploy ML-powered features. You will work with Python libraries, ML pipelines, REST APIs, and cloud deployments, collaborating with analysts and developers in a hands-on, remote-friendly environment.

Candidates with 0–2 years of experience, a solid grounding in math, statistics, and Python, are encouraged to apply.

Qualifications

• Bachelor's degree in CS/Math/Engineering or related field.
• 0–2 years of experience in ML/AI or relevant projects.
• Strong programming skills in Python.
• Understanding of ML algorithms and fundamental concepts.
• Knowledge of supervised and unsupervised learning techniques.
• Basic statistics, probability, and linear algebra.
• Familiarity with NumPy, Pandas, and Scikit-learn.
• Knowledge of TensorFlow or PyTorch is a plus.
• Understanding of data preprocessing and feature engineering.
• Familiarity with model evaluation metrics and validation techniques.
• Basic knowledge of SQL and databases.
• Familiarity with Git and GitHub.
• Knowledge of REST APIs, Docker, or cloud deployment is an advantage.
• Strong analytical, problem-solving, and debugging skills.
• Good communication and teamwork abilities.
• Ability to work independently and collaboratively in a remote environment.

Responsibilities

• Collect, clean, preprocess, and analyze structured and unstructured datasets.
• Develop and implement machine learning models for classification, regression, clustering, and prediction.
• Apply supervised and unsupervised learning techniques.
• Perform feature engineering, feature selection, and data transformation.
• Train, validate, and evaluate machine learning models using appropriate performance metrics.
• Experiment with different algorithms, architectures, and hyperparameters.
• Develop reusable machine learning pipelines and workflows.
• Work with Python libraries and machine learning frameworks.
• Analyze model performance and identify opportunities for improvement.
• Integrate trained models into backend applications and APIs.
• Assist with deploying machine learning models into production environments.
• Monitor model performance, accuracy, and data quality.
• Document experiments, model configurations, and technical implementations.
• Collaborate with software engineers to integrate AI-powered functionality into applications.
• Research emerging machine learning, deep learning, and generative AI technologies.
• Follow responsible AI practices, including data privacy, bias awareness, and model reliability.

Skills

Python programming

Analytical thinking

Communication

Teamwork

Remote work

Education

Bachelor's degree in CS/Math/Engineering

Tools

NumPy

Pandas

Scikit-learn

TensorFlow

PyTorch

SQL

Jupyter Notebook

REST APIs

Docker

Cloud platforms

Git

GitHub

Job description

Join Data Eminence as a remote Machine Learning Engineer and contribute to developing intelligent, data-driven solutions that automate processes, identify patterns, and support informed business decisions. You will work on data preprocessing, model development, training, evaluation, optimization, and deployment of machine learning solutions.

The ideal candidate has a strong foundation in Python, mathematics, statistics, and machine learning concepts. This role provides hands-on experience with real-world datasets, predictive modeling, machine learning pipelines, AI technologies, and software integration.

Full Description

At Data Eminence, we use data and emerging technologies to develop practical solutions for complex business problems. As a Machine Learning Engineer, you will collaborate with data analysts, software developers, and other technical teams to design, implement, and maintain machine learning models and intelligent applications.

Your responsibilities will include:

• Collect, clean, preprocess, and analyze structured and unstructured datasets.
• Develop and implement machine learning models for classification, regression, clustering, and prediction.
• Apply supervised and unsupervised learning techniques.
• Perform feature engineering, feature selection, and data transformation.
• Train, validate, and evaluate machine learning models using appropriate performance metrics.
• Experiment with different algorithms, architectures, and hyperparameters.
• Develop reusable machine learning pipelines and workflows.
• Work with Python libraries and machine learning frameworks.
• Analyze model performance and identify opportunities for improvement.
• Integrate trained models into backend applications and APIs.
• Assist with deploying machine learning models into production environments.
• Monitor model performance, accuracy, and data quality.
• Document experiments, model configurations, and technical implementations.
• Collaborate with software engineers to integrate AI-powered functionality into applications.
• Research emerging machine learning, deep learning, and generative AI technologies.
• Follow responsible AI practices, including data privacy, bias awareness, and model reliability.

We believe in learning by doing. You will gain practical experience with Python, NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, SQL, Jupyter Notebook, feature engineering, model evaluation, REST APIs, Docker, cloud platforms, ML pipelines, and AI-assisted development tools.

This is a hands-on role where you will work through the complete machine learning lifecycle, from preparing data and experimenting with algorithms to evaluating models and integrating intelligent solutions into real applications.

Requirements

• Bachelor's degree in Computer Science, Information Technology, Engineering, Mathematics, Statistics, or a related field.
• 0–2 years of experience in machine learning, artificial intelligence, data science, or relevant projects.
• Strong programming skills in Python.
• Understanding of machine learning algorithms and fundamental concepts.
• Knowledge of supervised and unsupervised learning techniques.
• Basic understanding of statistics, probability, and linear algebra.
• Familiarity with NumPy, Pandas, and Scikit-learn.
• Knowledge of TensorFlow or PyTorch is a plus.
• Understanding of data preprocessing and feature engineering.
• Familiarity with model evaluation metrics and validation techniques.
• Basic knowledge of SQL and databases.
• Familiarity with Git and GitHub.
• Knowledge of REST APIs, Docker, or cloud deployment is an advantage.
• Strong analytical, problem-solving, and debugging skills.
• Good communication and teamwork abilities.
• Ability to work independently and collaborate effectively in a remote environment.
• Competitive salary based on skills and experience.
• Fully remote work flexibility.
• Paid time off and holidays.
• Professional development and learning opportunities.
• Company-provided equipment where applicable.
• Exposure to modern machine learning, AI, and cloud technologies.
• Opportunities to work on real-world machine learning projects.
• Supportive and collaborative remote work environment.