Machine Learning Engineer

Role Overview :

We are looking for a talented Machine Learning Engineer to design, develop, deploy, and optimize machine learning solutions that drive business value through predictive analytics, automation, and intelligent decision-making. The ideal candidate will have strong expertise in machine learning algorithms, model development, data processing, and MLOps practices, with the ability to build scalable and production-ready AI solutions.

Key Responsibilities :

- Design, develop, train, and deploy machine learning models to solve complex business problems.

- Perform data preprocessing, feature engineering, data validation, and exploratory data analysis to improve model performance.

- Build and optimize predictive models for classification, regression, forecasting, recommendation, anomaly detection, and other use cases.

- Develop scalable ML pipelines for model training, evaluation, deployment, and monitoring.

- Work with large datasets to extract meaningful insights and improve business outcomes.

- Implement model performance tracking, monitoring, and continuous improvement processes.

- Collaborate with data engineers to build robust data pipelines and ensure data quality.

- Integrate machine learning models into enterprise applications and production environments.

- Conduct model testing, validation, and hyperparameter optimization to enhance accuracy and efficiency.

- Support automation initiatives by leveraging AI and machine learning technologies.

- Stay updated with advancements in machine learning, artificial intelligence, and data science methodologies.

- Document model development processes, assumptions, evaluation results, and deployment procedures.

Required Skills & Experience :

- 5-10 years of experience in Machine Learning, Artificial Intelligence, Data Science, or related fields.

- Strong proficiency in Python and machine learning development.

- Hands-on experience with machine learning frameworks and libraries such as :

1. Scikit-learn

2. TensorFlow

3. PyTorch

- Strong understanding of supervised and unsupervised learning techniques.

- Experience in feature engineering, model evaluation, model optimization, and performance tuning.

- Knowledge of statistical analysis, probability, and predictive modeling techniques.

- Experience working with SQL and large-scale datasets.

- Understanding of data structures, algorithms, and software engineering best practices.

- Strong analytical, problem-solving, and communication skills.