Key Strengths

Demonstrated experience with Apache Spark, HDFS, and Hive for big data processing.
Proficiency in Python for data analysis, machine learning, and LLM applications.
Exposure to Scala for Spark ETL and Akka HTTP server development.
Understanding of distributed computing concepts through Spark projects.

Cultural & Operational Fit

Cultural Fit Analysis

The candidate's projects are heavily focused on data engineering, machine learning, and big data, primarily using Python and Scala. While there is a stated target role of 'Java Backend developer' and an 'IBM Java Backend developer' experience entry, the project portfolio does not align with core Java backend development. This indicates a potential mismatch with the target role's primary technology stack, suggesting a lower cultural fit for a dedicated Java backend position without further evidence of Java proficiency.

Soft Skills & Operational Fit

Insufficient data to assess soft skills and operational fit. The candidate's project descriptions indicate an ability to work with complex technical topics, but no direct evidence of collaboration, problem-solving under pressure, or communication in a team setting is available.

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jebasingh wesley j

Java Backend developer

https://www.opentalent.in/jebasingh-wesley-j

Welcome to my GitHub page! Here, you'll find a variety of open source projects that reflect my passion for collaborative development and innovation.

IBM

chennaiMember since June 12, 2026

https://www.opentalent.in/jebasingh-wesley-j

Top Skills

PythonScala

Skills

PythonScala

Experience

IBM

Java Backend developer

June 12, 2026 – Present

Projects

Preprocessing-Unstructured-Data-for-LLM-Applications-RAG

December 3, 2024 – December 3, 2024

Preprocessing-Unstructured-Data-for-LLM-Applications-RAG Here’s a detailed README for your project. It highlights the key features, dependencies, usage, and instructions to work with PDFs containing text, images, and tables.

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loRA-LLM-Fine-tuning

December 3, 2024 – December 3, 2024

This repository contains a step-by-step guide to fine-tuning Large Language Models (LLMs) using Low-Rank Adaptation (LoRA). The goal is to train and evaluate domain-specific LLMs with a centralized approach, leveraging the robust ChatGLM2-6B base model and medAlpaca dataset for practical examples.

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Linear-Regression-Pyspark

June 13, 2023 – August 6, 2024

In statistics, linear regression is a linear approach for modelling the relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables). The case of one explanatory variable is called simple linear regression; for more than one, the process is called multiple linear regression.

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Pyspark-Streaming-Bank-Analysis

April 17, 2023 – August 6, 2024

This project marks our initial venture into Apache Spark. We focused on online banking analysis by leveraging various datasets from Kaggle, including loans, customer credit cards, and transactions. After downloading these datasets, we cleaned the data and utilized tools and technologies like Spark, HDFS, and Hive to execute various use cases.

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PySpark-Dataframe-Guide

April 15, 2023 – August 6, 2024

PySpark Dataframe Complete Guide (with COVID-19 Dataset) Spark which is one of the most used tools when it comes to working with Big Data. While once upon a time Spark used to be heavily reliant on RDD manipulations, Spark has now provided a DataFrame API for us Data Scientists to work with.

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PySpark-SQL

April 13, 2023 – August 6, 2024

Apache Spark is a cluster computing system that offers comprehensive libraries and APIs for developers. SparkSQL is a module in Apache Spark for processing structured data with the help of the DataFrame API.

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Hadoop-Hive-SQL-Scala

March 20, 2023 – August 6, 2024

This repository contains resources and examples for working with Hadoop, Hive, and Spark, with a focus on SQL operations and Scala-based implementations. It also covers the basics of Spark job submission and scheduling with Cron.

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Key Strengths

Demonstrated experience with Apache Spark, HDFS, and Hive for big data processing.
Proficiency in Python for data analysis, machine learning, and LLM applications.
Exposure to Scala for Spark ETL and Akka HTTP server development.
Understanding of distributed computing concepts through Spark projects.

Cultural & Operational Fit

Cultural Fit Analysis

Soft Skills & Operational Fit