1. MODEL DEVELOPMENT AND DEPLOYMENT:

• Design, develop, and train machine learning models for specific use cases, such as classification, regression, clustering, natural language processing, or computer vision.
• Optimize model performance and accuracy through hyperparameter tuning and advanced techniques.
• Deploy machine learning models into production environments using tools such as TensorFlow Serving, PyTorch, Docker, or Kubernetes.

2. DATA MANAGEMENT SKILLS:

• Hands-on experience with data manipulation and analysis using tools like Pandas, NumPy, and SQL.
• Knowledge of big data technologies such as Hadoop, Spark, or Kafka is a plus.

4. ANALYTICAL SKILLS:

• Proficient in statistics, probability, and optimization techniques.
• Experience with visualization tools like Matplotlib, Seaborn, or Tableau.

PREFERRED QUALIFICATIONS:

• Certifications in AI/ML technologies or cloud platforms (e.g., AWS Certified Machine Learning, Google Cloud ML Engineer).
• Experience with natural language processing (NLP) or computer vision applications.
• Knowledge of MLOps practices, CI/CD pipelines, and version control systems like Git.
• Background in a specialized field such as robotics, autonomous systems, or financial modeling.