As a Machine Learning Engineer , you will design, develop, and deploy intelligent machine learning solutions that solve complex business problems using data-driven approaches. You will build predictive models, develop scalable ML pipelines, optimize model performance, and collaborate with data scientists, software engineers, and product teams to deliver production-ready machine learning applications.

Your responsibilities may include data preprocessing, feature engineering, model training, evaluation, deployment, monitoring, and continuous improvement of machine learning systems. You will also work with modern ML frameworks, cloud platforms, and MLOps tools to ensure reliable, scalable, and high-performance AI solutions.

Responsibilities

- Design, develop, and deploy machine learning models for real-world applications.
- Collect, clean, preprocess, and transform structured and unstructured datasets.
- Perform feature engineering and data analysis to improve model performance.
- Train, evaluate, and optimize machine learning and deep learning models.
- Develop scalable ML pipelines for data processing, model training, and inference.
- Implement predictive analytics, classification, regression, clustering, and recommendation systems.
- Integrate machine learning models into production applications using REST APIs and backend services.
- Monitor model performance, detect model drift, and continuously improve prediction accuracy.
- Collaborate with data scientists, AI engineers, software developers, and product teams.
- Write clean, maintainable, and well-documented code following software engineering best practices.
- Deploy ML models using cloud platforms, Docker, and MLOps workflows.




- Stay updated with the latest machine learning research, frameworks, and industry advancements.

Requirements

- Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering, Information Technology, Mathematics, or a related field.
- Strong analytical thinking and problem-solving skills.
- Positive understanding of Python programming.
- Basic understanding of Machine Learning, Deep Learning, and Artificial Intelligence concepts.
- Familiarity with data preprocessing, feature engineering, and model evaluation techniques.
- Understanding of statistics, probability, and linear algebra fundamentals.
- Knowledge of data structures, algorithms, and object-oriented programming.
- Familiarity with SQL and databases.
- Excellent written and verbal communication skills.
- Internship, academic projects, or machine learning experience is preferred but not mandatory.

Preferred Qualifications

- Experience with Scikit-learn, TensorFlow, PyTorch, or Keras.
- Familiarity with Pandas, NumPy, Matplotlib, and data visualization libraries.
- Experience building predictive models for classification, regression, clustering, or recommendation systems.
- Knowledge of Natural Language Processing (NLP), Computer Vision, or Time Series Forecasting.
- Familiarity with cloud platforms such as AWS, Azure,



or Google Cloud Platform.
- Experience deploying ML models using Docker, FastAPI, Flask, or Kubernetes.
- Understanding of MLOps concepts including MLflow, model versioning, CI/CD, and monitoring.
- Experience with Git and collaborative software development workflows.
- Familiarity with big data technologies such as Spark or Hadoop is a plus.
- Strong attention to detail with a passion for building reliable and scalable ML systems.

Skills

- Machine Learning
- Artificial Intelligence
- Deep Learning
- Python
- Scikit-learn
- TensorFlow
- PyTorch
- Keras
- Pandas
- NumPy
- Matplotlib
- Data Preprocessing
- Feature Engineering
- Model Evaluation
- Statistics
- SQL
- REST APIs
- FastAPI
- Docker
- MLOps
- MLflow
- Git & Version Control
- Cloud Computing
- Problem Solving
- Team Collaboration
- Communication Skills

Compensation Projects are compensated based on complexity and duration, with earnings ranging from ₹140/hour to ₹420/hour.

What You'll Gain

- Build production-ready machine learning solutions for real-world business applications.
- Gain hands-on experience with industry-leading ML frameworks, cloud platforms, and MLOps tools.
- Learn scalable model deployment, monitoring, and optimization techniques.
- Work alongside experienced AI engineers, data scientists, and software developers.
- Develop expertise in predictive analytics, deep learning, NLP, computer vision, and intelligent automation.
- Build a strong foundation for career growth in Machine Learning Engineering, Data Science, AI Engineering, MLOps Engineering, Research Engineering, or AI Solutions Architecture.

📌 Machine Learning Engineer (India)
🏢 Data Eminence
📍 India