Machine Learning Engineer (Entry-Level Junior)

• Location: Coimbatore
• Work Mode: On-site
• Experience: 1 1.5 Years

We are looking for a Machine Learning Engineer with early experience in building end-to-end ML solutions for data-driven decision-making. The role involves working across the ML lifecycle including data processing model development deployment and performance monitoring. The candidate will collaborate with cross-functional teams to deliver scalable and reliable machine learning systems.

• Develop and implement machine learning models using appropriate algorithms and techniques
• Perform exploratory data analysis (EDA) and data preprocessing
• Conduct feature engineering and model selection for optimal performance
• Train validate and fine-tune machine learning models
• Deploy machine learning models in production environments (cloud or on-premises)
• Monitor model performance and implement improvements or retraining strategies
• Work with large datasets using distributed computing frameworks
• Conduct A/B testing and evaluate different model architectures
• Implement time series models for forecasting and predictive analysis
• Collaborate with data engineers and software teams for integration
• Maintain documentation for models experiments and workflows
• Ensure scalability reliability and performance of ML systems

• Bachelors degree in Computer Science Electronics Data Science or related field
• 1 1.5 years of hands-on experience in machine learning or data science
• Experience working on end-to-end ML projects (academic or industry)
• Understanding of statistical modeling and machine learning fundamentals

Programming & Libraries:

• Python (NumPy Pandas Scikit-learn)

Machine Learning Frameworks:

• TensorFlow PyTorch Keras

Machine Learning Concepts:

• Supervised and Unsupervised Learning
• Reinforcement Learning (basic understanding)
• Model evaluation and hyperparameter tuning

Data Engineering & Big Data:

• Hadoop Spark

Data Analysis & Visualization:

• Matplotlib Seaborn Tableau Power BI

Specialized Areas:

• Time Series Analysis and Forecasting
• A/B Testing and Experimentation

Deployment & Infrastructure:

• Model deployment (cloud platforms or on-premises systems)

• Experience with containerization (Docker)
• Exposure to Kubernetes or MLOps practices
• Experience with real-time data processing pipelines
• Knowledge of version control systems (Git)
• Exposure to cloud platforms (AWS / Azure / GCP)
  
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