About the Job:
As a Machine Learning Engineer, you will design, develop, and deploy machine learning models for a variety of business and IT optimization challenges. You will collaborate closely with data scientists, software engineers, and product owners to bring AI-powered features into production.
Key Responsibilities:
- Design and implement scalable machine learning models and pipelines.
- Work with large, structured and unstructured datasets.
- Train, validate, and deploy models in cloud environments (AWS, Azure, or GCP).
- Collaborate with MLOps teams to automate deployment and monitoring.
- Continuously evaluate and improve the performance of AI systems.
- Document models, experiments, and pipelines for reproducibility and compliance.
- Participate in code reviews and knowledge sharing sessions.
Qualifications & Experience:
- Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field.
- 3+ years of experience in Machine Learning or AI roles.
- Proficiency in Python and common ML libraries (e.g., scikit-learn, TensorFlow, PyTorch).
- Experience with cloud platforms (AWS Sagemaker, Azure ML, or GCP AI Platform).
- Strong grasp of algorithms, model evaluation metrics, and data preprocessing.
- Experience with data engineering tools (e.g., Pandas, Spark, SQL).
- Knowledge of NLP, computer vision, or time-series forecasting.
- Experience with MLOps tools (MLflow, Kubeflow, Airflow, Docker).
- Familiarity with CI/CD for ML pipelines.
- Experience working in Agile or Scrum teams.
