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 equipment

Job summary

Data Eminence is seeking a remote Machine Learning Engineer to design and deploy data-driven solutions across the lifecycle.

You will work on data prep, model development, evaluation, and deployment, collaborating with engineers and analysts to integrate ML into products. The role emphasizes Python, ML tooling, and scalable pipelines, with full remote flexibility and ongoing professional development.

Qualifications

• Bachelor's degree in CS, IT, engineering, math, or statistics.
• 0–2 years of ML, data science, AI, or relevant projects.
• Strong Python programming skills.
• Solid understanding of ML fundamentals and common algorithms.
• Experience with supervised/unsupervised learning.
• Familiarity with NumPy, Pandas, Scikit-learn.
• Knowledge of model evaluation metrics.
• Experience with REST APIs, SQL, and data visualization.

Responsibilities

• Prepare data by collecting, cleaning, and preprocessing.
• Develop ML models for real-world use cases.
• Experiment with algorithms and tune configurations.
• Evaluate models using appropriate metrics.
• Build ML pipelines and deploy models.
• Create APIs or services for model inference.
• Collaborate with software teams to integrate ML into products.
• Document experiments and maintain reproducible workflows.
• Monitor models post-deployment and address performance issues.

Skills

Python

Statistics

Machine Learning

Data Analysis

Education

Bachelor's degree

Tools

NumPy

Pandas

Scikit-learn

TensorFlow/PyTorch

Jupyter

SQL

Git/GitHub

Docker

REST APIs

Cloud platforms

Job description

Join Data Eminence as a remote Machine Learning Engineer and contribute to building intelligent, data-driven solutions that can automate processes, identify patterns, and support better decision-making. You will work across data preparation, model development, experimentation, evaluation, and deployment of machine learning solutions.

The ideal candidate has a strong foundation in Python, statistics, and machine learning concepts and enjoys solving real-world problems using data. This role provides hands‑on experience with machine learning workflows, data analysis, model optimization, APIs, deployment, and modern AI technologies.

Full Description

At Data Eminence, we use data and intelligent technologies to develop practical solutions for real-world business problems. As a Machine Learning Engineer, you will work closely with developers, data analysts, and other teams to prepare data, develop machine learning models, evaluate their performance, and integrate them into usable applications.

Your responsibilities will include:

• Collect, clean, preprocess, and analyze structured and unstructured datasets.
• Develop and implement machine learning models for real-world use cases.
• Apply supervised and unsupervised learning techniques where appropriate.
• Perform feature engineering and feature selection.
• Train, validate, and evaluate machine learning models using appropriate metrics.
• Experiment with different algorithms and model configurations.
• Identify patterns, trends, and relationships within datasets.
• Use statistical and analytical techniques to support model development.
• Optimize models for accuracy, performance, scalability, and reliability.
• Build data‑processing and machine learning pipelines.
• Integrate trained models into applications and backend services.
• Develop APIs or services for machine learning model inference.
• Track experiments, document results, and maintain reproducible workflows.
• Monitor deployed models and identify potential performance issues.
• Work with databases and data‑storage systems to access and manage datasets.
• Collaborate with software developers to integrate ML functionality into products.
• Research emerging machine learning and AI techniques.
• Use AI‑assisted development and research tools where appropriate.
• Maintain technical documentation for models, experiments, and implementation processes.

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

This is a hands‑on role where you will work through the complete machine learning lifecycle—from preparing raw data and experimenting with models to evaluating results and integrating machine learning 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, data science, AI, or relevant academic/personal projects.
• Strong programming skills in Python.
• Understanding of machine learning fundamentals and common algorithms.
• Knowledge of supervised and unsupervised learning techniques.
• Understanding of statistics, probability, and basic linear algebra.
• Familiarity with NumPy, Pandas, and Scikit-learn.
• Familiarity with TensorFlow or PyTorch is a plus.
• Basic understanding of data preprocessing and feature engineering.
• Knowledge of model evaluation metrics and validation techniques.
• Familiarity with SQL and databases.
• Understanding of data visualization using tools such as Matplotlib or similar libraries.
• Familiarity with Git and GitHub.
• Knowledge of REST APIs, Docker, or cloud platforms is a plus.
• Strong analytical, problem‑solving, and debugging skills.
• Good written and verbal communication skills.
• 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 ML and AI projects.
• Supportive and collaborative remote work environment.