Job Description:
Responsibilities:
- Develop and optimize machine learning models, deep learning systems, and AI-driven applications.
- Collaborate with data scientists, architects, and developers to build end-to-end AI pipelines.
- Lead experimentation, model evaluation, and hyperparameter tuning to maximize model performance.
- Deploy machine learning models in production environments using best MLOps practices.
- Conduct research on emerging AI technologies and bring innovative ideas into production workflows.
- Mentor junior AI engineers and contribute to internal AI knowledge sharing.
Preferred Qualifications:
- Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or related field.
- Proficiency with frameworks like TensorFlow, PyTorch, scikit-learn, HuggingFace, and XGBoost.
- Hands-on experience with model deployment frameworks like TensorFlow Serving, TorchServe, ONNX, or MLflow.
- Strong knowledge of Python (must-have), and experience with other programming languages (e.g., Java, C++) is a plus.
- Solid experience with cloud-based AI services (AWS SageMaker, Azure ML, Google Vertex AI).
- Familiarity with data engineering workflows, feature engineering, and model monitoring techniques.
- Strong mathematical foundation in statistics, linear algebra, and optimization methods.
Excellent problem-solving skills and the ability to translate business problems into AI solutions.





