Job Title: Machine Learning Engineer
Job Summary:
We are looking for a Junior Machine Learning Engineer to develop, optimize, and deploy machine learning models for real-world applications. The ideal candidate should have hands-on experience with data preprocessing, model training, deep learning frameworks, and model deployment. This role involves collaborating with data scientists, software engineers, and business teams to build intelligent, scalable, and high-performance AI solutions.
Key Responsibilities:
- Collect, clean, and preprocess datasets for machine learning applications
- Develop and optimize machine learning models, including supervised and unsupervised learning techniques
- Implement deep learning models using TensorFlow, PyTorch, and Scikit-learn
- Perform feature engineering, dimensionality reduction, and data augmentation
- Optimize model hyperparameters and performance using techniques like GridSearchCV and Bayesian Optimization
- Deploy ML models using Flask, FastAPI, or cloud-based solutions (AWS SageMaker, Google Cloud AI, Azure ML)
- Work with NLP, computer vision, and time-series forecasting depending on the project requirements
- Analyze model performance using evaluation metrics (accuracy, precision, recall, RMSE, AUC-ROC, F1-score)
- Collaborate with data engineers to build data pipelines and ensure efficient model deployment
- Stay updated on emerging AI trends, ML algorithms, and industry best practices
Skills and Knowledge Required:
- Proficiency in Python and ML libraries (Scikit-learn, TensorFlow, PyTorch, Keras)
- Understanding of machine learning algorithms, including regression, classification, clustering, and reinforcement learning
- Experience with deep learning models (CNNs, RNNs, Transformers, LSTMs)
- Knowledge of model evaluation techniques, overfitting prevention, and regularization
- Hands-on experience with data preprocessing, feature extraction, and data augmentation
- Experience with SQL and NoSQL databases for handling large datasets
- Basic knowledge of cloud computing and MLOps (AWS SageMaker, Google Cloud AI, Azure ML)
- Ability to deploy ML models using REST APIs, Docker, or cloud-based solutions
- Understanding of DevOps principles, version control (Git), and CI/CD for ML pipelines
- Strong analytical and problem-solving skills
Educational Qualifications:
- Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, Statistics, or a related field
- Certifications in Machine Learning, Deep Learning, or AI (AWS, Google, Coursera, Udacity) are a plus
Experience:
- 1-2 years of experience in machine learning, data science, or AI-driven application development
- Experience in handling real-world datasets, implementing models, and optimizing AI performance
Key Focus Areas:
- Supervised & Unsupervised Learning Models
- Deep Learning & Neural Networks
- Model Optimization & Hyperparameter Tuning
- ML Model Deployment & Integration
Tools and Technologies:
- Programming Languages: Python
- ML Frameworks: Scikit-learn, TensorFlow, PyTorch, Keras
- Data Processing & Visualization: Pandas, NumPy, Matplotlib, Seaborn
- Cloud Platforms: AWS SageMaker, Google Cloud AI, Azure ML (optional)
- Deployment Tools: Flask, FastAPI, Docker, Kubernetes
- Version Control & MLOps: Git, DVC, MLflow
Other Requirements:
- Ability to work independently and in a team environment
- Passion for AI-driven solutions and continuous learning
- Strong documentation and communication skills