Machine Learning Engineer (Entry-Level / Junior)

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  • Coimbatore, Tamil Nadu
  • Permanent
  • Full-time
  • 12 hours ago
  • Apply easily
Machine Learning Engineer (Entry-Level / Junior)
  • Location: Coimbatore
  • Work Mode: On-site
  • Experience: 1 1.5 Years
1. Role OverviewWe 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. 2. Key Responsibilities
  • 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
3. Required Qualifications
  • Bachelor's 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
4. Technical SkillsProgramming & 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)
5. Good to Have (Optional)
  • 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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