Job Summary:
We are seeking a talented Machine Learning Engineer with 26 years of experience to design, develop, and deploy machine learning models and data-driven solutions. The ideal candidate will have hands-on experience in Python, machine learning frameworks, data preprocessing, feature engineering, and model deployment, along with the ability to collaborate with data scientists, software engineers, and business stakeholders.
Key Responsibilities:
- Develop, train, and optimize machine learning models for predictive and prescriptive analytics.
- Preprocess, clean, and engineer features from structured and unstructured data.
- Collaborate with data scientists, data engineers, and software teams to deploy ML models in production environments.
- Implement state-of-the-art machine learning algorithms and techniques suitable for the business problem.
- Monitor model performance, accuracy, and drift, retraining models as required.
- Ensure data integrity, security, and compliance with privacy regulations.
- Integrate machine learning models into applications, APIs, or microservices for real-time or batch predictions.
- Write efficient, maintainable, and reusable code following software engineering best practices.
- Participate in research and evaluation of new ML frameworks, algorithms, and tools.
Required Qualifications:
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or related field.
- 26 years of professional experience in machine learning, data science, or AI engineering.
- Strong programming skills in Python or R.
- Hands-on experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
- Experience in data preprocessing, feature engineering, and exploratory data analysis (EDA).
- Understanding of model evaluation metrics, hyperparameter tuning, and cross-validation.
- Experience with deploying models using Docker, Kubernetes, or cloud services
Preferred Skills (Nice to Have):
- Experience with deep learning models, NLP, or computer vision.
- Knowledge of cloud platforms (AWS Sagemaker, Azure ML, GCP AI Platform).
- Exposure to MLOps practices and automated ML pipelines.
- Familiarity with CI/CD for ML models.
