Description :

Key Responsibilities

- Develop, train, and deploy machine learning models into scalable, production-ready environments.

- Perform end-to-end data workflows including data collection, cleaning, preprocessing, and feature engineering.

- Conduct Exploratory Data Analysis (EDA) to uncover patterns, trends, and insights that drive business decisions.

- Build, optimize, and evaluate supervised, unsupervised, and deep learning models using modern ML frameworks.

- Design and implement automated ML pipelines using MLOps tools and CI/CD best practices.

- Collaborate with cross-functional teams to translate business requirements into data-driven solutions.

- Monitor model performance in production and execute periodic retraining, tuning, and maintenance.

- Create dashboards, reports, and visualizations to clearly communicate analytical findings to stakeholders.

- Work with cloud platforms (AWS, GCP, Azure) to develop and deploy ML solutions efficiently.

- Manage version control, documentation, and experimentation tracking using Git and MLflow or similar tools.

- Explore advanced ML domains such as NLP, Computer Vision, or Time-Series to support upcoming initiatives.

- Handle large-scale datasets and work with distributed computing frameworks like Spark or Hadoop when required.

Requirements & Qualifications:

Education:

- Bachelors or Masters degree in Computer Science, Data Science, Engineering, or a related technical field.

Technical Experience:

- 5+Years years of hands-on experience developing, training, and deploying machine learning models in production environments.

- Proficiency in Python and major ML frameworks such as Scikit-learn, TensorFlow, PyTorch, or Keras.

- Strong experience with data manipulation and analysis using Pandas and NumPy.

- Skilled in data visualization using libraries such as Matplotlib and Seaborn.

- Solid understanding of supervised, unsupervised, and deep learning techniques.

- Experience performing end-to-end Data Analytics, including data cleaning, trend analysis, reporting, and insight generation.

Core Data Skills:

- Strong capability in Exploratory Data Analysis (EDA).

- Experience with feature engineering and data preprocessing.

- Good grounding in statistics and mathematical concepts relevant to machine learning.

MLOps & Deployment :

- Familiarity with MLOps tools such as MLflow, Kubeflow, or similar platforms.

- Experience working with cloud services (AWS, GCP, or Azure) for model development and deployment.

- Proficiency with version control systems (Git) and CI/CD practices for ML pipelines.

Preferred Qualifications :

- Exposure to NLP, Computer Vision, or Time-Series modeling.

- Experience working with large-scale distributed systems or Big Data technologies like Spark or Hadoop.

- Knowledge of data warehousing solutions and SQL.