Resume Examples & Writing Guide
I have around 7+ years of experience as Software Engineer(Java/Python/Golang) by working into multiple technologies in Ecommerce and Fintech Domain.Experience with data engineering, machine learning, deep learning. Love to work on building backend/machine learning,data engineering in its entirety
GitHub : https://github.com/vinay10949
Contact No: +919769711499/ +917021334841
Motive & Profile Go-getter, passionate about technology, innovation. Eager to utilize my technical skills combine with AI/ML domain knowledge and create practical products for the real world.Willing to develop values under company culture.
7+ years of programming experience in languages like Java,Python,Golang. Experience on building BackEnd Systems,Microservices,Recommendation Engine, Loan Default Prediction Modules, SMS Profiling ,Fashion Recommendation etc using Data and Machine Learning.
Sklearn,Numpy,Pandas,rapids,scipy, Keras, Tensorflow ,and Big data analysis skill with Python on multiple scenarios. Have attended few Kaggle competition
Built complete backend. Built RealTime Notification Engine that powers customer reminder notifications for repayments and eligibility for new loan product.
Built Calibrated ML model that predicts the probability of customer likely to default a short term loan.
Built Computer Vision Module for identify aadhar and pan card, cheque and doing OCR on to Pan and aadhar. Also built scalable distributed systems.
Also built a simple SMS profiling using regex.
Worked here as software engineer,worked in core team of building recommedation engine with low latency. Worked on building backend services.
Worked on cold start problem of Music Recommendation Engine using audio features using vienna university library,then used machine learning to recommend music.
Worked on bookmyshow realtime seat occupancy module that show real time seat occupancy on to bookmyshow app
Worked for client Mercedes ,helped them with building marketing dashboards in Qlikview
Github: https://github.com/vinay10949/CVD
Role- End to End Deployment,Testing,Monitoring,Retraining
Responsible Detailed EDA,Model Creation,deployment,testing of model,monitoring and retraining approach
Achieved Recall of 82% ,using F2 score as metric.
Identifying eye descriptors and then calculated EAR I.e eye aspect ratio for consecutive frame to identify if person in frame is blinking his eyes or not
Built face verification module to verify profile photos with document photos.
Built name and address similarity between provided name and name in the document using combination of cosine similarity and Levenshtein Distance .
Objective was to perform data engineering ,data pipeline and detailed analysis of our supermoney customers.
Also predict which first time Uber Customer is likely to default 5000 Rupee product loan.
Achievement : achieved 0.79 F-Beta Score , loan default rate dropped by 40%.
Objective was to predict testing Time for various combination of congurations..This could be useful to priortize the congurations to be tested that could save most of our time. - Given a set of feature variables predict testing time.
Github : https://github.com/vinay10949/AnalyticsAndML/tree/master/Kaggle/Mercedes-Benz_Greener_Manufacturing
Built a machine learning model to predict
whether two questions asked on quora are
similar or not . So that the similar questions asked
may have the same answers which have been
given earlier for the previously asked similar
question.
Github : https://github.com/vinay10949/AnalyticsAndML/tree/master/Kaggle/quora-question-pairs
Challenge was given various data like
applicants- information about each loan
application at Home Credit)
bureau,we had to predict what is the application's
capability to repay the loan.
Achievement : Was in top 30%
Github : https://github.com/vinay10949/AnalyticsAndML/tree/master/Kaggle/HomeCreditRisk
Used a pretrained Vgg16 model to extract visual features from outt , extracted text features from descriptions of outt like like weighted tdfword2vec
Used combination of above features to nd the immediate neighbours tried both using Euclidean distance,and also trained LSH model above high dimensional of combined features to find immediate neighbours of outfit.
Futher future ongoing work:
Collecting fashion documents to train a word2vec
on all fashion corpus documents like vogue etc,
Convert a user text or query into a elements,style
document query. Use outt as sequence of items
and train a sequential model as recurrent
networks, or graph Neural networks to solve
problem like recommending an outt as
sequence of items,or if any one item is changed
new sequence should be considered.
Created ML Recommendation engine for music,used Music Information Retrieval (MIR) from libary by Vienna university,used features like rhythm pattern,temporal descriptor,etc(approv 2000dimensions of one song) Used Locality Senstive Hashing algorithm +Episilon Greedy Approach for recommendation
Developed Timeband rules for users booking their events on BookMyShow,Developed system which recommended nearby venues,Also developed Recommendation system which used Movie Ratings to generate recommedations,Calculated Bayesian average of reviews to recommend top events (Movie,Plays and Concerts) in a region
Technology :
Python,Redis,MongoDB,Php
Achievement Built one core component for Recommendation Engine with very low latency
Created Golang gRPC powered micro-service for extracting data from documents like Aadhar Pan,voterid using OCR.
Also created aadhar esigning document flow for signing documents via NSDL.
Created Golang gRPC powered micro-service for video kyc for Smart Background verification . Used protocal buffers
and created grpc based Unary API. Designed High Level Architecture, written docker script and CI /CD Pipeline.
Created and handling entire backend for product called SuperMoney,the entire rest apis uses Spring Hibernate. Experience in Spring Framework such as Spring MVC. Strong hands - on experience with Spring IO, Spring Boot
Created SMS proling application using regular expression and Named Entity Recoginition using python NLTK
Built robust grpc based microservices,built using Golanguage and GOA framework and developed corresponding test cases .
Client ResearchNow.
Built an highly scalable logger service using Golang that can handle million writes per 10sec,used channels extensively,Wrote code for log rotation ,log compression .
Created Golang grcpc webservice that uses vault to run authentication service.Wrote service that creates users,assign roles,create certificates,authenticate using certificate etc
Client : F5
Built Golang webservice that uses GoogleCloud pub sub for creating Notication system Wrote GRPC based webservice in golang and created processor which listens to subscriber and sends notication to users.
Role- Was responsible for creating and deployment of system into docker end to end. Was also partly responsible for High Level System Design
Client : Schlumberger
Built an logger service in Golang that can handle million writes of gps coordinates per 10sec,used channels extensively,Wrote code for detailed summary stats of gps coordinates like Distance travelled in a day ,
Which coordinate is likely to be his home.
Role- Was responsible for creating and deployment of system into docker end to end. Was also partly responsible for High Level System Design
Client : Supermoney
Built a entire seat occupancy module that pinged 2500 cinemas by generated schedules,and capture the real time seat occupancy at that particular time ,all that information was stored in queuing system which was later consumed by SeatOccupancy engine that shows real time seat allocation in cinemas.
Also solved race conditions when concurrently many users were booking a seat which happened during peak season
Technology Nodejs,RabbitMQ,Mysql,Golang
Client : Bookmyshow
Computer Science Major
DOB :25-08-1989
Birth Place :Mumbai.
Languages: English, Hindi and Marathi.
Marital status : Married
Making resumes a piece of cake
