R	a n j i t h   K u m a r	 K.	N	. 	
  Bengaluru	, Karnataka	, Indi	a         	 LinkedIn/Ra	njith	-Kumar	   	  GitHub/R	anjith	-Kumar	          	 +91	 90087	69944	 	   ranjith.go226	0@gmail.com	 	
E x p e r i e n c e	 	
Data Engineer	 | Pravaig 	Dynamics |	 Bengaluru, 	Karnataka,	 India	                                                                       	          	February 202	3 - Prese	nt      	                                       	
&bull; 	Spearheaded  data  engineering  initiatives  within  the  Electric  Vehicle  Research  &amp;  Development  team	 and 	ensure  seamless  integration  of  data  engineering 	
solutions with EV R&amp;	D, enabling informed decision	-making based on data insights.	 	
&bull; 	Developed and put into use data pipelines for vehicle data collecting that make effective use of technologies such as Kafka.	 	
&bull; 	Integrat	ion of CAN	-BUS, Radar,	 GPS and camera data into the data processing workflow, optimizing data ingestion and analysis	. 	
&bull; 	Leveraged ROS2 framework for processi	ng IMU data, ensuring seamless integration and compatibility within the data ecosystem.	 	
&bull; 	Implemen	ted and maintained automated data processing workflows using Jenkins, enhancing overall efficiency and reducing processing ti	mes.	 	
&bull; 	Managed AWS infrastr	ucture  to support scalable data storage and  computation needs for EV data analytics.	 Developed and maintained an  in	-house data 	
warehouse to centralize and optimize data storage, retrieval, and analysis.	 	
 
Big Data	 Engineer (	Apprenticeship)	 | T	ata	 Consultancy	 Services	 |Bengaluru, 	Karnataka,	 India	            	                  	April	 2022 	&ndash; October 	2022	 	
 	
&bull; 	Developed  and  maintained  scalable  data  pipelines  using  Hadoop,  Spark,  and  oth	er  Big  Data  technologies  to  process  large  volumes  of  data,  resulting  in 	increased  efficiency  by  30%.	 Conducted  data  analys	is  and  visualization  to  extract  actionable  insights,  aiding  stakeholders  in  making  informed  business 	decisions.	 	 	
Research Scholar 	- Data Scienc	e | MENMOZHI 	TECHNOLOGIE	S |Bengaluru	, Karnataka,	 India	             	                 	 February	 2022 	&ndash; May	 2022	 	
 	
&bull; 	Experience  in  the  space  of  Software  Development  in  Computer  Vision	 utilizing  a  m	ix  of  customary  Image  Processing  based  and  current	 Machine 	Learning/Deep Learning	-based methods.	 	 	
 Skills	 
&bull; 	Programming	 Skills	: Python	 (Pandas,	 NumPy,	 Matplotlib	), Machine	 Leaning	, Deep	 Leaning	 (CNN),	 Tensor	Flow, PyTorch	, Deep Leaning, Scala,	 	
R Langua	ge. 	
&bull; 	 	o	 	 HTML, REAC	T, CSS, DJANGO, FLASK, FAST 	API.	  	
&bull; 	Database	 Skill	s: SQL,	 MongoDB,	 Redis	, Hadoop	, Hive	, NoSQL.	 	
&bull; 	Miscellaneous	 Skills	: Git,	 Docker	, Applied	 Statistics,	 Power	 BI, Jenkins	,  Circle  CI	, Rabbi	tMQ	, Kafka, Ansi	ble	, Airflo	w, ML	-flow, 	AWS	, Azure, 	
Mu	ltiprocessing, Statistical Modeling, Hypothesis Testing	, Clustering	, ETL, 	Object Detection, NLP, NMT	. 	
&bull; 	 	Time 	Ma	nagement, Teamwork, Proble	m-solving, Docum	entation, Scum	, Meeting and Engaging Presentation. 	
E d u c a t i o n	 	
St. Joseph&#039;s	 University	, Bengaluru	                                                                              	                            	Sheshadripuram	 Collage	, Bengaluru	 	MSc,	 Big Data	 Analytics	, 2020	 &ndash; 2022	       	                                                                                              	Bachelor	 of Computer	 Applications	, 2016	 &ndash; 2019	 	
P r o j	e c t s	 	
A study on dog emotion recognition using deep learning tec	hniques	 (AIP (American Institute of Physics)	 Recognized):	 	
The purpose of this research was to determine if a deep learning system can 	classify	 various	 facial emotion classes in dogs, such as happiness,	 	sadness, anger, and neutral. For	 this, we proposed three different convolutional net	works. The findings prove that, when	 compared to the other models, the suggested 	
model MBCC	-CNN outperforms in terms of	 facial expression recognition in dogs	. Link	 to paper	:  AIP	 Paper	 link	 	
 
Neural	-Machine	-Translation: Models: Transformers, Bert, SMT, Seq2Seq	: 	
 
Natural machine translation refers to the process of using computational algorithms and artificial intelligence to translate	. I have used 	BERT, Seq2Seq, and Transformers	 	models	 which	 all are 	significant models in the field of natural language processing (NLP), particularly in machine translation.	 In practice, these models can be combin	ed 	
or used in different parts of a machine translation system	.  Project	 link	:  NMT	 Proj. link	 	
 C e r t i f i c a t e s	 	
&bull; Python Data Structures Coursera 	-- Sept 2021: 	 Link                                            	           	 &bull;  Data Analytics Virtual Experience Program	: Link	 	
&bull; The Sparks Foundation Virtual Internship 	-- October 2021:	  Link                      	           	  &bull;  Data Science	 using	 R: Link