Stack
Available for Projects
Stack
Available for Projects
Stack
Available for Projects
I rely on a carefully curated stack of technologies, frameworks, and services to deliver robust, scalable, and user-centric solutions. By integrating best practices from coding, data science, and MLOps, each project is executed with efficiency, security, and measurable impact.
Tech Stack
UI/UX Stack
Programming & Core Development
Python
I use Python as a powerful and versatile programming language for developing scalable applications, automating workflows, and performing advanced data analysis. Its extensive ecosystem of libraries and frameworks supports my work in web development, machine learning, and AI.
Golang
Go is my choice for building fast and reliable backend systems. I use its concurrency features and simplicity to develop high-performance microservices and cloud-native applications that can scale effortlessly.
Operating System & Version Control
Linux
I work with Linux as the backbone of server environments, container platforms, and development systems. Its open-source flexibility and robust security allow me to customize, optimize, and manage critical infrastructure.
Git
I use Git to manage code changes and collaborate effectively on projects. It enables me to track revisions, experiment with branches, and maintain a clean and reliable codebase.
GitHub
I use GitHub to host and manage repositories, leveraging its collaboration tools for version control and project tracking. With GitHub Actions, I automate workflows like CI/CD, streamlining integration and deployment processes.
Web Development Frameworks
Flask
Flask helps me create lightweight and flexible web applications and APIs. I use it for rapid prototyping and deploying scalable services while retaining full control over the application architecture.
Django
I leverage Django to build robust, feature-rich web applications that include authentication, ORM, and admin interfaces. Its comprehensive framework allows me to develop scalable and secure solutions efficiently.
FastAPI
I use FastAPI to create high-performance APIs that prioritize speed and reliability. Its data validation features and auto-generated documentation enhance my development workflow and simplify collaboration.
Data Science Libraries
Pandas
Pandas is my go-to library for cleaning, analyzing, and transforming structured data. Its powerful DataFrame structures allow me to handle large datasets efficiently, preparing them for deeper analysis or machine learning tasks.
NumPy
I rely on NumPy for numerical computing, leveraging its multi-dimensional arrays and mathematical functions. It forms the computational backbone of my data science and machine learning workflows.
scikit-learn
I use scikit-learn to implement and fine-tune machine learning models for tasks like classification, regression, and clustering. Its simplicity and versatility enable me to prototype and deploy models quickly.
Matplolib
Matplotlib helps me create detailed and customizable visualizations to explore and present data. I use it to craft insightful plots and graphs that effectively communicate complex findings.
Seaborn
Seaborn simplifies statistical data visualization, allowing me to quickly identify patterns and relationships in data. Its high-level interface enhances my exploratory analysis process with elegant and informative plots.
Core DevOps Tools
Docker
I use Docker to containerize applications and their dependencies, ensuring consistent environments from development to production. Its portability enables me to deploy and scale solutions effortlessly.
Jenkins
Jenkins automates my CI/CD pipelines, streamlining testing, building, and deploying applications. It helps me maintain a seamless development workflow by automating repetitive tasks.
Ansible
I rely on Ansible to automate configuration management, software deployment, and system orchestration. Its simplicity and agentless architecture make it an integral part of my infrastructure workflows.
Terraform
I use Terraform to define and manage Infrastructure as Code [IaC], enabling reproducible and scalable deployments across multiple cloud environments. Its declarative syntax simplifies complex provisioning tasks.
Kubernetes
Kubernetes is my choice for orchestrating containerized applications, automating scaling, and ensuring high availability. It empowers me to manage cloud-native systems with efficiency and reliability.
Cloud Platforms
Amazon Web Services [AWS]
I utilize AWS for its robust computing, storage, and AI/ML capabilities. I leverage services like SageMaker for training and deploying machine learning models, while its global infrastructure supports scalable and reliable applications.
Google Cloud Platform [GCP]
I rely on GCP for secure and scalable cloud infrastructure, analytics, and AI solutions. Tools like Vertex AI help me build end-to-end machine learning workflows, integrating seamlessly into cloud-native applications.
Data Enginering Tools
Apache Spark
I use Apache Spark for distributed data processing and large-scale analytics. Its in-memory computing allows me to handle complex big data tasks efficiently, including batch and streaming workflows.
Apache AirFlow
Apache Airflow helps me orchestrate workflows and automate data pipelines. Its intuitive scheduling and monitoring features make managing complex ETL processes seamless and efficient.
Kafka
I rely on Kafka to manage real-time data streams and event-driven architectures. It ensures high throughput and fault tolerance for integrating dynamic data systems in real time.
Machine Learning Frameworks
TensorFlow
TensorFlow is my tool of choice for building and deploying machine and deep learning models. Its flexibility and scalability allow me to handle projects ranging from AI research to production-ready applications.
PyTorch
I use PyTorch for its dynamic computation graph and ease of use, enabling rapid experimentation in machine learning. It’s ideal for both research and deploying scalable AI systems.
Natural Language Processing [NLP] Libraries
spaCy
spaCy allows me to process and analyze text efficiently, handling tasks like tokenization, named entity recognition, and dependency parsing. Its pre-trained pipelines streamline my NLP workflows.
Hugging Face Transformers
Hugging Face Transformers enables me to leverage state-of-the-art pre-trained language models for NLP tasks like text generation, classification, and translation. It simplifies advanced NLP integrations into my projects.
Machine Learning Operations and Observability
Databricks
Databricks is a unified platform I use to streamline data engineering, analytics, and machine learning workflows. Built on Apache Spark, it allows me to process big data efficiently while collaborating on scalable AI projects through shared workspaces and automated pipelines.
MLFlow
I rely on MLFlow to manage the machine learning lifecycle, from tracking experiments to deploying and monitoring models. Its ability to integrate with tools like TensorFlow and PyTorch simplifies the management of model versions and production workflows.
KubeFlow
Kubeflow helps me orchestrate and manage machine learning workflows on Kubernetes, ensuring scalability and reproducibility. I use it to streamline complex AI pipelines, from training and hyperparameter tuning to deployment and monitoring in cloud-native environments.
Prometheus
Prometheus is my go-to tool for real-time monitoring and alerting, allowing me to collect and query metrics from infrastructure and applications. Its flexible data model and robust alerting rules help ensure high availability and system reliability.
Grafana
Grafana enables me to visualize metrics and build interactive, real-time dashboards for monitoring system performance. By integrating with data sources like Prometheus, it provides actionable insights and proactive alerts for infrastructure and applications.
Tech Stack
UI/UX Stack
Programming & Core Development
Python
I use Python as a powerful and versatile programming language for developing scalable applications, automating workflows, and performing advanced data analysis. Its extensive ecosystem of libraries and frameworks supports my work in web development, machine learning, and AI.
Golang
Go is my choice for building fast and reliable backend systems. I use its concurrency features and simplicity to develop high-performance microservices and cloud-native applications that can scale effortlessly.
Operating System & Version Control
Linux
I work with Linux as the backbone of server environments, container platforms, and development systems. Its open-source flexibility and robust security allow me to customize, optimize, and manage critical infrastructure.
Git
I use Git to manage code changes and collaborate effectively on projects. It enables me to track revisions, experiment with branches, and maintain a clean and reliable codebase.
GitHub
I use GitHub to host and manage repositories, leveraging its collaboration tools for version control and project tracking. With GitHub Actions, I automate workflows like CI/CD, streamlining integration and deployment processes.
Web Development Frameworks
Flask
Flask helps me create lightweight and flexible web applications and APIs. I use it for rapid prototyping and deploying scalable services while retaining full control over the application architecture.
Django
I leverage Django to build robust, feature-rich web applications that include authentication, ORM, and admin interfaces. Its comprehensive framework allows me to develop scalable and secure solutions efficiently.
FastAPI
I use FastAPI to create high-performance APIs that prioritize speed and reliability. Its data validation features and auto-generated documentation enhance my development workflow and simplify collaboration.
Data Science Libraries
Pandas
Pandas is my go-to library for cleaning, analyzing, and transforming structured data. Its powerful DataFrame structures allow me to handle large datasets efficiently, preparing them for deeper analysis or machine learning tasks.
NumPy
I rely on NumPy for numerical computing, leveraging its multi-dimensional arrays and mathematical functions. It forms the computational backbone of my data science and machine learning workflows.
scikit-learn
I use scikit-learn to implement and fine-tune machine learning models for tasks like classification, regression, and clustering. Its simplicity and versatility enable me to prototype and deploy models quickly.
Matplolib
Matplotlib helps me create detailed and customizable visualizations to explore and present data. I use it to craft insightful plots and graphs that effectively communicate complex findings.
Seaborn
Seaborn simplifies statistical data visualization, allowing me to quickly identify patterns and relationships in data. Its high-level interface enhances my exploratory analysis process with elegant and informative plots.
Core DevOps Tools
Docker
I use Docker to containerize applications and their dependencies, ensuring consistent environments from development to production. Its portability enables me to deploy and scale solutions effortlessly.
Jenkins
Jenkins automates my CI/CD pipelines, streamlining testing, building, and deploying applications. It helps me maintain a seamless development workflow by automating repetitive tasks.
Ansible
I rely on Ansible to automate configuration management, software deployment, and system orchestration. Its simplicity and agentless architecture make it an integral part of my infrastructure workflows.
Terraform
I use Terraform to define and manage Infrastructure as Code [IaC], enabling reproducible and scalable deployments across multiple cloud environments. Its declarative syntax simplifies complex provisioning tasks.
Kubernetes
Kubernetes is my choice for orchestrating containerized applications, automating scaling, and ensuring high availability. It empowers me to manage cloud-native systems with efficiency and reliability.
Cloud Platforms
Amazon Web Services [AWS]
I utilize AWS for its robust computing, storage, and AI/ML capabilities. I leverage services like SageMaker for training and deploying machine learning models, while its global infrastructure supports scalable and reliable applications.
Google Cloud Platform [GCP]
I rely on GCP for secure and scalable cloud infrastructure, analytics, and AI solutions. Tools like Vertex AI help me build end-to-end machine learning workflows, integrating seamlessly into cloud-native applications.
Data Enginering Tools
Apache Spark
I use Apache Spark for distributed data processing and large-scale analytics. Its in-memory computing allows me to handle complex big data tasks efficiently, including batch and streaming workflows.
Apache AirFlow
Apache Airflow helps me orchestrate workflows and automate data pipelines. Its intuitive scheduling and monitoring features make managing complex ETL processes seamless and efficient.
Kafka
I rely on Kafka to manage real-time data streams and event-driven architectures. It ensures high throughput and fault tolerance for integrating dynamic data systems in real time.
Machine Learning Frameworks
TensorFlow
TensorFlow is my tool of choice for building and deploying machine and deep learning models. Its flexibility and scalability allow me to handle projects ranging from AI research to production-ready applications.
PyTorch
I use PyTorch for its dynamic computation graph and ease of use, enabling rapid experimentation in machine learning. It’s ideal for both research and deploying scalable AI systems.
Natural Language Processing [NLP] Libraries
spaCy
spaCy allows me to process and analyze text efficiently, handling tasks like tokenization, named entity recognition, and dependency parsing. Its pre-trained pipelines streamline my NLP workflows.
Hugging Face Transformers
Hugging Face Transformers enables me to leverage state-of-the-art pre-trained language models for NLP tasks like text generation, classification, and translation. It simplifies advanced NLP integrations into my projects.
Machine Learning Operations and Observability
Databricks
Databricks is a unified platform I use to streamline data engineering, analytics, and machine learning workflows. Built on Apache Spark, it allows me to process big data efficiently while collaborating on scalable AI projects through shared workspaces and automated pipelines.
MLFlow
I rely on MLFlow to manage the machine learning lifecycle, from tracking experiments to deploying and monitoring models. Its ability to integrate with tools like TensorFlow and PyTorch simplifies the management of model versions and production workflows.
KubeFlow
Kubeflow helps me orchestrate and manage machine learning workflows on Kubernetes, ensuring scalability and reproducibility. I use it to streamline complex AI pipelines, from training and hyperparameter tuning to deployment and monitoring in cloud-native environments.
Prometheus
Prometheus is my go-to tool for real-time monitoring and alerting, allowing me to collect and query metrics from infrastructure and applications. Its flexible data model and robust alerting rules help ensure high availability and system reliability.
Grafana
Grafana enables me to visualize metrics and build interactive, real-time dashboards for monitoring system performance. By integrating with data sources like Prometheus, it provides actionable insights and proactive alerts for infrastructure and applications.
Tech Stack
UI/UX Stack
Programming & Core Development
Python
I use Python as a powerful and versatile programming language for developing scalable applications, automating workflows, and performing advanced data analysis. Its extensive ecosystem of libraries and frameworks supports my work in web development, machine learning, and AI.
Golang
Go is my choice for building fast and reliable backend systems. I use its concurrency features and simplicity to develop high-performance microservices and cloud-native applications that can scale effortlessly.
Operating System & Version Control
Linux
I work with Linux as the backbone of server environments, container platforms, and development systems. Its open-source flexibility and robust security allow me to customize, optimize, and manage critical infrastructure.
Git
I use Git to manage code changes and collaborate effectively on projects. It enables me to track revisions, experiment with branches, and maintain a clean and reliable codebase.
GitHub
I use GitHub to host and manage repositories, leveraging its collaboration tools for version control and project tracking. With GitHub Actions, I automate workflows like CI/CD, streamlining integration and deployment processes.
Web Development Frameworks
Flask
Flask helps me create lightweight and flexible web applications and APIs. I use it for rapid prototyping and deploying scalable services while retaining full control over the application architecture.
Django
I leverage Django to build robust, feature-rich web applications that include authentication, ORM, and admin interfaces. Its comprehensive framework allows me to develop scalable and secure solutions efficiently.
FastAPI
I use FastAPI to create high-performance APIs that prioritize speed and reliability. Its data validation features and auto-generated documentation enhance my development workflow and simplify collaboration.
Data Science Libraries
Pandas
Pandas is my go-to library for cleaning, analyzing, and transforming structured data. Its powerful DataFrame structures allow me to handle large datasets efficiently, preparing them for deeper analysis or machine learning tasks.
NumPy
I rely on NumPy for numerical computing, leveraging its multi-dimensional arrays and mathematical functions. It forms the computational backbone of my data science and machine learning workflows.
scikit-learn
I use scikit-learn to implement and fine-tune machine learning models for tasks like classification, regression, and clustering. Its simplicity and versatility enable me to prototype and deploy models quickly.
Matplolib
Matplotlib helps me create detailed and customizable visualizations to explore and present data. I use it to craft insightful plots and graphs that effectively communicate complex findings.
Seaborn
Seaborn simplifies statistical data visualization, allowing me to quickly identify patterns and relationships in data. Its high-level interface enhances my exploratory analysis process with elegant and informative plots.
Core DevOps Tools
Docker
I use Docker to containerize applications and their dependencies, ensuring consistent environments from development to production. Its portability enables me to deploy and scale solutions effortlessly.
Jenkins
Jenkins automates my CI/CD pipelines, streamlining testing, building, and deploying applications. It helps me maintain a seamless development workflow by automating repetitive tasks.
Ansible
I rely on Ansible to automate configuration management, software deployment, and system orchestration. Its simplicity and agentless architecture make it an integral part of my infrastructure workflows.
Terraform
I use Terraform to define and manage Infrastructure as Code [IaC], enabling reproducible and scalable deployments across multiple cloud environments. Its declarative syntax simplifies complex provisioning tasks.
Kubernetes
Kubernetes is my choice for orchestrating containerized applications, automating scaling, and ensuring high availability. It empowers me to manage cloud-native systems with efficiency and reliability.
Cloud Platforms
Amazon Web Services [AWS]
I utilize AWS for its robust computing, storage, and AI/ML capabilities. I leverage services like SageMaker for training and deploying machine learning models, while its global infrastructure supports scalable and reliable applications.
Google Cloud Platform [GCP]
I rely on GCP for secure and scalable cloud infrastructure, analytics, and AI solutions. Tools like Vertex AI help me build end-to-end machine learning workflows, integrating seamlessly into cloud-native applications.
Data Enginering Tools
Apache Spark
I use Apache Spark for distributed data processing and large-scale analytics. Its in-memory computing allows me to handle complex big data tasks efficiently, including batch and streaming workflows.
Apache AirFlow
Apache Airflow helps me orchestrate workflows and automate data pipelines. Its intuitive scheduling and monitoring features make managing complex ETL processes seamless and efficient.
Kafka
I rely on Kafka to manage real-time data streams and event-driven architectures. It ensures high throughput and fault tolerance for integrating dynamic data systems in real time.
Machine Learning Frameworks
TensorFlow
TensorFlow is my tool of choice for building and deploying machine and deep learning models. Its flexibility and scalability allow me to handle projects ranging from AI research to production-ready applications.
PyTorch
I use PyTorch for its dynamic computation graph and ease of use, enabling rapid experimentation in machine learning. It’s ideal for both research and deploying scalable AI systems.
Natural Language Processing [NLP] Libraries
spaCy
spaCy allows me to process and analyze text efficiently, handling tasks like tokenization, named entity recognition, and dependency parsing. Its pre-trained pipelines streamline my NLP workflows.
Hugging Face Transformers
Hugging Face Transformers enables me to leverage state-of-the-art pre-trained language models for NLP tasks like text generation, classification, and translation. It simplifies advanced NLP integrations into my projects.
Machine Learning Operations and Observability
Databricks
Databricks is a unified platform I use to streamline data engineering, analytics, and machine learning workflows. Built on Apache Spark, it allows me to process big data efficiently while collaborating on scalable AI projects through shared workspaces and automated pipelines.
MLFlow
I rely on MLFlow to manage the machine learning lifecycle, from tracking experiments to deploying and monitoring models. Its ability to integrate with tools like TensorFlow and PyTorch simplifies the management of model versions and production workflows.
KubeFlow
Kubeflow helps me orchestrate and manage machine learning workflows on Kubernetes, ensuring scalability and reproducibility. I use it to streamline complex AI pipelines, from training and hyperparameter tuning to deployment and monitoring in cloud-native environments.
Prometheus
Prometheus is my go-to tool for real-time monitoring and alerting, allowing me to collect and query metrics from infrastructure and applications. Its flexible data model and robust alerting rules help ensure high availability and system reliability.
Grafana
Grafana enables me to visualize metrics and build interactive, real-time dashboards for monitoring system performance. By integrating with data sources like Prometheus, it provides actionable insights and proactive alerts for infrastructure and applications.