SHASHANK MISHRA

About

A highly motivated and skilled individual with a passion for technology and a proven track record of success in software development and data analysis.

Work

Nielsen Media, Bangalore
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Member of Technical Staff – 1

Summary

Worked in the Data Preparation and Factories team handling batch data processing and computation. Used Python, Java, Spark, SQL for batch data processing and computation, and Airflow for orchestration.

Highlights

Worked in the Data Preparation and Factories team handling batch data processing and computation.

Used Python, Java, Spark, SQL for batch data processing and computation, and Airflow for orchestration.

Qen Labs Inc., California
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Software Development Intern

Summary

Spearheaded App Development on GIS Crowdsourcing and researched existing projects. Developed using Flutter/Dart for Frontend, worked with Map APIs, Backend was written in Django, hosted on AWS.

Highlights

Spearheaded App Development on GIS Crowdsourcing and researched existing projects.

Developed using Flutter/Dart for Frontend, worked with Map APIs, Backend was written in Django, hosted on AWS.

Gymo Wellness Solutions LLP, Jorhat
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Flutter Developer

Summary

Utilised Golang (Gin Framework) to write RESTful APIs on the backend. Worked on MVC Architecture. Incorporated Rule Engine (Grule) to handle numerous validations and rules. Led UI Designing, Complex State Management (more than 50 state variables across 10+ different screens), Map APIs, Firebase (Authentication and Firestore), Payment Gateway Integration (with 20+ payment methods)

Highlights

Utilised Golang (Gin Framework) to write RESTful APIs on the backend.

Worked on MVC Architecture. Incorporated Rule Engine (Grule) to handle numerous validations and rules.

Led UI Designing, Complex State Management (more than 50 state variables across 10+ different screens), Map APIs, Firebase (Authentication and Firestore), Payment Gateway Integration (with 20+ payment methods)

Unacademy
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Student Mentor

Summary

Mentored (educating and doubt solving) more than 100 with clear and concise documentation and video solutions for their queries.

Highlights

Mentored (educating and doubt solving) more than 100 with clear and concise documentation and video solutions for their queries.

Education

Indian Institute of Information Technology, Kota, India

→

B.Tech

CSE

Grade: 8.3 / 10

Central Academy, Jhunsi, Prayagraj, India

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High School

CBSE (Class XII)

Grade: 94.6%

Central Academy, Jhunsi, Prayagraj, India

→

High School

CBSE (Class X)

Grade: 9.8 / 10

Skills

Programming Languages

C++, C, Dart, Python, Java, Scala, Golang, SQL.

Frameworks

SpringBoot, Gin-Gonic, Hibernate, Flutter.

Libraries and Tools

Resilience4J, OpenFeign, RabbitMQ, Spark, Git.

Orchestration and Cloud

AWS, Airflow, Docker, Kubernetes.

Databases

Postgres, Firebase, MongoDB.

Concepts and Principles

Distributed Systems, Operating Systems, Computer Networks, Data Structures and Algorithms, DBMS, OOPs.

Projects

Social Media Trends Extraction using Google Vision API

Summary

Automated Trend Analysis: Executed Instagram APIs for real-time trend data, coupled with Google Vision API to classify clothing images by their properties. This resulted in a 2x faster trend analysis compared to traditional methods. Dynamic Product Recommendations: Integrated the above results to Flipkart's API to seamlessly generate buying links for products similar to extracted trends. Use of Vision API improved the efficiency by 15%. User-Centric Customization: Option for users to upload device images, extending the search functionality to their local device. Tech Stack: The Backend was written in Flask, and the frontend was made using React Native. Deployment on Heroku

Time Series Annotation Using Active Learning

Summary

Utilized K-Means Clustering with Active Learning Strategy on PCR melting dataset of 10 million data points for efficiency. Incorporated "Uncertainty Sampling" Active Learning strategy. This strategy was 20% more optimized than other conventional methods. Detection and Removal of Outlier Time Series curves using Local Outlier Factor Algorithm.

Job Application Backend System

Summary

Microservices Architecture: Consisting of 3 independent microservices (Job, Company, Review) using Spring Boot. Utilized OpenFeign for inter-service communication and RabbitMQ for message queuing. Fault Tolerance and Service Discovery: Integrated Resilience4J for circuit breakers, retries, and rate limiting. Employed Netflix Eureka for service discovery and Zipkin for distributed tracing. Containerized Infrastructure: Deployed using Postgres for the database and Docker containers for Postgres and Zipkin, ensuring scalable and reliable service orchestration using Kubernetes.