Sunil Jangra

• Designed and Developed Spark Pipelines to Observe customer repayment behaviour.
• Have Developed Pipelines to generate performance report of all digital communication with customer like SMS, Email, WhatsApp and Push Notification.
• Have re-structured existing pipelines to improve processing performance.
• Collaborated with team on ETL (Extract, Transform, Load) tasks, maintaining data integrity and verifying pipeline stability.
• Developed ETLs to find out delinquent customers for. PP and PL.
• Performed large-scale data conversions, transferring raw data into standardised formats for integration into PL and PP.
• Have written Jenkins jobs to automate deployments for DE and ML.

Currently working one of LTI product Mosaic-Decision.

In this project i contribute by developing REST APIs in Java programming language. Debug code on daily basis. I prepare Technical Design Documents for new future requirements.

Apart from that i look after docker containers for their healthiness.

Woked on a project iMEC (Multi-Access Edge Computing ).

As a Machine Learning Engineer I have developed model for Security and Device activity analysis.

To enhance security, I have design a Model to find out number of person exists in thermal images and movement of person a-428a28136/ using OpenCv utility.

Developed tree based models to detect abnormal behaviour of the devices and generate respected alerts.

As a Big Data Developer I have handled data management. Streaming Data using Kafka consumer in our application. Apart from that I have helped my team writing REST APIs in Go Lang.

I have written an algorithm to remove sonar issues from source code file.

During my employment, I have worked on the company's product Method83.

Method83 is complete package to deploy any IoT solution.
Under this project as a Data Engineer I have written multiple
algorithms in form of ETLs using Spark Framework in Java and
Python. eg;- Anomaly detection, Device monitoring, Tenant and Device connectivity mapping etc.

During development I have used Apache Kafka to publish and
consume data into distributed environment and used MQTT to
consume data from different sensors.
Apart from these I have written ETL wrapper which helps us to mold our raw data from different clients to the form which is compatible with Mehtod83.

As a Machine Learning Engineer, I have trained multiple Linear and Logistic Regressor to trigger alarm to prevent device failure/downtime. For Method83 I have built Machine Learning Studio under my lead (Amitabh Chandra) supervision. In this activity i have handled resource management, implemented pre-processing techniques. Apart from these I have written an algorithm which can Generate MongoDB query to flat the nested structure of the data fo r machine learning algorithms.