Machine Learning Engineer - Python

About The Role

Key Responsibilities

• Design, develop, and deploy machine learning and deep learning models.
• Conduct advanced data preprocessing, feature engineering, and model evaluation.
• Implement optimization techniques for algorithm performance and scalability.
• Work with Python frameworks such as Scikit-learn, TensorFlow, PyTorch, and Keras for model development.
• Build interactive dashboards using Streamlit, Plotly, and other visualization tools.
• Collaborate with cross-functional teams including data engineering, product, and software development.
• Research and implement state-of-the-art ML/AI algorithms.
• Deploy models using FastAPI, Flask, Docker, or Kubernetes.
• Monitor and maintain production models for performance and reliability.
  

Required Skills & Expertise

• Programming Languages : Python (NumPy, Pandas, Matplotlib, Seaborn, etc.), SQL.
• Machine Learning : Supervised & unsupervised learning, regression, classification, clustering, recommendation systems, ensemble methods, Scikit-learn.
• Deep Learning : CNNs, RNNs, LSTMs, GRUs, Transformers, attention mechanisms, TensorFlow, PyTorch, Keras.
• Natural Language Processing (NLP) : Text preprocessing, embeddings, transformers (BERT, GPT), sentiment analysis, NER.
• Computer Vision : Image classification, object detection, segmentation.
• Time Series Analysis : Forecasting using ARIMA, Prophet, LSTM, RNN.
• Algorithms & Data Structures : Strong foundation in algorithm design, complexity analysis, probability, statistics, and optimization.
• MLOps / Deployment : FastAPI/Flask, Docker, Kubernetes, CI/CD pipelines, cloud deployment (AWS/GCP/Azure).
• Data Management : SQL, NoSQL, ETL pipelines, big data frameworks (Spark, Hadoop).
• Visualization & Reporting : Streamlit, Plotly, Matplotlib, Seaborn, interactive dashboards.