go_to_languages:
  - Python
  - Javascript
  - C

cloud_services:
  description: Hands-on experience with various AWS services
  services:
    - AWS CloudFormation: Managing and provisioning your AWS infrastructure as code.
    - AWS IAM: Managing access and permissions for your AWS resources.
    - AWS Cognito: Adding authentication and authorization to your applications.
    - AWS S3: Scalable object storage for your applications.
    - AWS ECR: Managed container image registry.
    - AWS DynamoDB: NoSQL database for high-performance applications.
    - AWS RDS: Managed relational database service for MySQL, PostgreSQL, and other databases.
    - AWS Lambda: Running serverless functions in the cloud.
    - AWS EC2: Scalable virtual servers in the cloud.
    - AWS ECS: Orchestrating and managing containerized applications.
    - AWS API Gateway: Building and managing APIs for your applications.
    - AWS SageMaker: Building, training, orchestrating and deploying machine learning models and pipelines.
    - AWS Bedrock: Fully managed service that offers a choice of high-performing foundation LLMs.

devops_and_fullstack:
  description: Libraries, Tools, Frameworks leveraged for DevOps and FullStack
  libraries_tools_frameworks:
    - Docker: Building and deploying applications in containers for easy scalability and portability.
    - CDK & Pulumi: Infrastructure as Code (IaC) tools for defining, deploying, and managing cloud infrastructure.
    - Serverless Framework: Simplifying the deployment and management of serverless applications.
    - GitHub Actions: Automating CI/CD workflows.
    - Flask & FastAPI: My go-to Python frameworks for building robust and efficient backends.
    - jQuery or React: Crafting interactive and responsive frontend experiences.
    - Streamlit: Transforming Python code into interactive web apps.

machine_learning:
  description: Libraries, Tools, Frameworks leveraged for Machine Learning, Deep Learning, Federated Learning, Data Science, and Data & MLOps.
  libraries_tools_frameworks:
    - Numpy, Scipy, Pandas and PySpark: Fundamental libraries for numerical computing and data manipulation.
    - Matplotlib, Plotly, and Seaborn: Creating stunning visualizations to gain insights from data.
    - DVC: Open-source, platform-agnostic library for version control of data.
    - Scikit-learn, XGBoost, and LightGBM: Powerful Machine Learning libraries for classification, regression, and more.
    - Keras, TensorFlow and PyTorch: Building, developing and training Deep Learning models.
    - Flower: Exploring Federated Learning for Distributed Machine Learning.
    - MLflow: Open-source platform for managing and tracking machine learning experiments.
    - LangFuse: Open-source platform for LLM monitoring, observability & tracing.
    - Hugging Face: Platform for building, training, and deploying any kind of Machine Learning models.
    - Unsloth: Optimization of the resources needed for training and finetuning of LLMs.
    - Langchain & Llamaindex: Orchestration frameworks for LLM based applications
    - Ragas: Framework that evaluates Retrieval Augmented Generation (RAG) systems.