Machine Learning Engineer
5 days ago
About the Role :
We are seeking highly experienced Senior Machine Learning Engineers to join our AI team. Candidates should have 4+ years of experience building and deploying production-grade ML models. You will be responsible for end-to-end ML solutions, data-driven projects, and creating scalable, high-performance AI systems.
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.
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