Machine Learning Deep Learning & Data Science

About the Role

Machine Learning, Deep Learning, and Data Science

Key Responsibilities

• Design, develop, and deploy

  machine learning and deep learning models

  for real-world applications.
• Collect, preprocess, and analyze structured and unstructured data.
  
• Build predictive and classification models, recommendation systems, and NLP/Computer Vision solutions.
  
• Collaborate with cross-functional teams (Data Engineers, Software Developers, Product Managers) to integrate models into production.
  
• Optimize model performance, scalability, and accuracy.
  
• Stay updated with the latest research and advancements in AI/ML/DL.

Required Skills & Qualifications

• Strong knowledge of

  Python

  and ML libraries (NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, Keras).
• Experience with

  deep learning architectures

  (CNNs, RNNs, Transformers, GANs, etc.).
• Solid understanding of

  data structures, algorithms, and statistical methods

  .
• Hands-on experience with data visualization tools (Matplotlib, Seaborn, Power BI/Tableau a plus).
  
• Knowledge of

  cloud platforms

  (AWS, Azure, GCP) and deployment (Docker, Kubernetes) preferred.
• Strong problem-solving skills and the ability to work in a fast-paced environment.
  
• Bachelors/Masters degree in Computer Science, Data Science, AI, or related field.

Good to Have

• Publications, research, or participation in Kaggle competitions.
  
• Experience with

  big data tools

  (Hadoop, Spark).
• Exposure to

  MLOps pipelines

  .

Perks & Benefits

• Competitive salary and performance-based incentives
  
• Exposure to cutting-edge AI/ML projects
  
• Learning & development support (certifications, workshops, conferences)
  
• Flexible work culture and collaborative team environmen