Data Scientist

About the Role:

Data Scientist

Key Responsibilities:

• Design, build, and deploy machine learning and deep learning models across NLP, Computer Vision, and Generative AI domains.
  
• Develop RESTful APIs using

  FastAPI

  to serve and integrate machine learning models into production systems.
• Apply

  OOPs principles

  to write clean, maintainable, and scalable code in Python.
• Use

  SQL

  for advanced data extraction, transformation, and analysis from large-scale databases.
• Build and fine-tune models using deep learning libraries (e.g., TensorFlow, PyTorch).
  
• Implement and optimize NLP pipelines (e.g., transformers, sentiment analysis, summarization) and CV models (e.g., classification, segmentation, OCR).
  
• Develop and deploy

  Generative AI

  solutions using state-of-the-art models (e.g., GPT, Diffusion Models, GANs).
• Utilize

  Azure

  or

  GCP

  services (e.g., AI Platform, Vertex AI, Azure ML Studio) for model training, deployment, and monitoring.
• Collaborate closely with engineering, product, and DevOps teams to deliver high-impact AI-driven features and products.
  
• Follow MLOps best practices to manage model lifecycle, including versioning, CI/CD pipelines, and real-time monitoring.
  

Required Skills & Qualifications:

• Bachelor's or Masters degree in Computer Science, Data Science, AI/ML, or a related field.
  
• 3+ years of experience in Data Science or Machine Learning roles.
  
• Strong programming skills in

  Python

  , with hands-on experience in libraries such as Pandas, NumPy, Scikit-learn, TensorFlow, or PyTorch.
• Solid expertise in writing and optimizing complex

  SQL

  queries.
• Deep understanding of

  Object-Oriented Programming (OOPs)

  concepts and best practices.
• Experience developing APIs using

  FastAPI

  or similar frameworks.
• Proven track record in

  NLP

  ,

  Computer Vision

  , and

  Generative AI

  (LLMs, GANs, Diffusion Models, etc.).
• Hands-on experience with cloud platforms:

  Microsoft Azure

  and/or

  Google Cloud Platform (GCP)

  .
• Familiarity with model deployment, containerization (Docker), and orchestration (Kubernetes).
  

Preferred Qualifications:

• Experience with

  MLOps

  tools and frameworks (e.g., MLflow, Kubeflow, Airflow).
• Knowledge of cloud-native AI tools like Azure Cognitive Services or Google Vertex AI.
  
• Background in deploying models at scale in production environments.
  
• Contributions to open-source projects, research publications, or personal AI projects.