Deepak is a Senior Python Developer with 6+ years of experience in web application and API development using Python, Flask, FastAPI, Django, and DRF. His skill set also includes databases (MySQL, PostgreSQL, MongoDB, Oracle) and cloud services, AWS, Azure, and Heroku. Deepak is proficient in data manipulation and analysis using Pandas, including data cleaning, transformation, and aggregation, numerical computing with NumPy, and large-scale data processing, distributed computing, and ETL operations with PySpark. He has hands-on experience in system, program, and server troubleshooting, code review, optimization for speed enhancing, and working with JSON and XML for data serialization, API communication, and structured data exchange.
Worked on the back end of several applications from the ground up using Django and Flask.
Set up databases in RDS and configured backups for S3 bucket.
Enabled message queuing for asynchronous communication between distributed systems.
Responsible for gathering requirements, design, development, testing, and deployment.
Facilitated pub/sub messaging for sending notifications and alerts across multiple services.
Streamlined task allocation and workflow for a team of 4 developers, resulting in a 30% reduction in project delivery time and increasing efficiency in meeting client deadlines.
Led daily requirement gathering sessions with cross-functional teams, resulting in streamlined project timelines and improved deliverable quality, reducing development cycle by 20%.
Attended daily EOD calls with clients to monitor work progress on projects.
Resolved issues and bugs on live calls with clients, reflecting changes in the server on an urgent basis.
Developed a SaaS application for school management with features such as attendance, timetable, exam, marksheet, and fees management.
Involved in the complete software development lifecycle including requirement gathering, system design, development, and testing. Modified Django software and developed CRUD methods in Active Record.
Built and automated deployment APIs and provided technical support for operations. Tech stack included Python, Django, HTML, CSS, JavaScript, unit testing, AWS, Git, PostgreSQL, and Jira.
DB Connect is a portal designed for agencies and in-house users from Dainik Bhaskar to manage advertisement release orders.
The project involved creating documentation, adding navigation, fixing error handling, creating test cases for all endpoints, and implementing Ajax call for low latency data fetching.
A new module was developed to autogenerate rate code based on multiple attributes, and major bugs were fixed across both frontend and backend.
Yard Manager is a web application developed to handle data and operations related to a yard, inclusive of a weighing scale display that connects with a physical scale via Socket.
Used Confluence for documentation and collaboration within Agile teams, managed code versioning with GitHub, and handled deployment to staging and production servers. Additionally, participated in project planning sessions.
Created APIs for the application on AWS Lambda, integrated API Gateway, and managed daily client requirements efficiently. The tech stack used includes Django, DRF, unit testing, AWS, Sockets, GitLab, PostgreSQL, and Jira.
Invoice Reader is a SaaS document AI solution that allows users to store invoice and receipt data on the cloud
Working in Agile/Scrum environments, the team integrated new features as per client requirements, handled bug fixes, code reviews, and deployments using tech stack including Flask, AWS, Heroku, MongoDB, GitLab, NumPy, JavaScript, HTML, CSS, and Jira
The project development involved delegation to a team of 4 developers and the integration of third-party APIs for features like authentication and login as well as JavaScript libraries for the front end
CCA is a tool designed to assist BPOs in monitoring the performance of their agents, and facilitating issue resolution for agents.
The project's architecture and blueprint were established, facilitating multiple services to interact with each other and generate output.
A series of integrations were carried out, encompassing a database, Redis, a backend framework, and third-party APIs, and utilizing tech stack including Python 3.10, FastAPI 0.110.2, PostgreSQL, Azure VM, GitHub, Redis, Jira, Twilio, Pydantic, SQL Models(ORM), Pandas, PySpark, and pydantic_settings.
