Saksham Agarwal

Product Manager

Bengaluru, Karnataka, India6 yrs 5 mos experience

Most Likely To SwitchHighly Stable

Key Highlights

Expert in integrating machine learning into SaaS products.
Proficient in multiple JavaScript frameworks and technologies.
Strong background in data mining and predictive analytics.

Stackforce AI infers this person is a SaaS Fullstack Developer with a focus on Machine Learning integration.

Contact

Skills

Core Skills

Javascript FrameworksMachine LearningWeb Development

Other Skills

BusinessGitTeamworkDesign PatternsSoftware DesignBackbone.jsReact.jsAgile Application DevelopmentFront-end DesignSoftware DevelopmentAlgorithmsTeachingPreactApplied MathematicsWritten Communication

About

As a member of technical staff in product development at Oracle Content Management (OCM), I lead the charge in developing cutting-edge features and functionalities for the product. My expertise spans core JavaScript and various frameworks, including Preact, Backbone, Knockout, and TypeScript. In addition to this, I harness the power of REST API calls from the UI to seamlessly integrate our product with Oracle Cloud-hosted CMS. Furthermore, I've had the privilege of diving into the exciting world of machine learning (ML) during my current role. I've worked extensively with Long Short-Term Memory (LSTM) and deep learning models, including LLM models seamlessly & integrating them into our product. My passion lies in creating efficient and innovative solutions, and I thrive in the dynamic team environment at Oracle, where we strive to achieve impactful outcomes. I completed my bachelor's degree in computer science at the National Institute of Technology, Tiruchirappalli in 2021. During my time there, I sharpened my skills in data mining, neural networks, and deep learning, garnering multiple certifications and honors in these domains. My journey also included valuable internship experiences at Oracle, Sasken Technologies, and IIT BHU, where I contributed to projects spanning web components, error prediction, and topic modeling.

Experience

6 yrs 5 mos

Total Experience

3 yrs 2 mos

Average Tenure

4 yrs 11 mos

Current Experience

Oracle

4 roles

Senior Member of Technical Staff, DataSafe

Sep 2024 – Present · 1 yr 9 mos

Member of Technical Staff, DataSafe

Aug 2023 – Sep 2024 · 1 yr 1 mo

Working as a Full Stack Developer and DevOps Engineer in Data Safe, Oracle's newest Database security product for on premise and cloud databases on OCI , Azure etc.

Member Technical Staff, Product Development, Oracle Content Management (OCM)

Jul 2021 – Aug 2023 · 2 yrs 1 mo

Engaged within the Oracle Content Management (OCM) team to spearhead the development of cutting-edge features and functionalities for the product.
Actively leveraging utilities of core JavaScript and an array of frameworks, including Preact, Backbone, Knockout, TypeScript, etc., to initiate REST API calls from the UI, lead the development of cutting-edge features, and achieve seamless integration with Oracle Cloud-hosted CMS.
Integrating machine learning (ML) technologies into OCM, including the creation of a "Smart Zoom Repository" project:

BusinessGitTeamworkDesign PatternsSoftware DesignBackbone.js+24

Summer Intern

May 2020 – Jul 2020 · 2 mos · Bengaluru, Karnataka

The Project aimed at delivering the existent Web Center Services as Web Components that are easily consumed inside Oracle Content and Site Experience (OCE – Web Center Team). The services that were created as Web Components matched in UI and functionality with the existent Web Center Services.
Web Components were written in Pure JavaScript making it compatible to integrate with any of the existent JavaScript frameworks (Ex: React, Vue, Angular). Backend Work (READ/WRITE) of a service for a user was implemented using Oracle Web Center REST Services. Finally, the created components were deployed on OCE (Oracle Content and Experience) Cloud by constructing an OCE Local Component. Knockout JS was used to add additional parameters having one-one mapping in OCE with the existing task flow parameters in Web Center Portal.

BusinessAgile Application DevelopmentFront-end DesignSoftware DevelopmentWritten CommunicationCascading Style Sheets (CSS)+3

Sasken technologies limited

Digital Solutions Internship

Dec 2019 – Jan 2020 · 1 mo · Bengaluru, Karnataka

The main aim of the project was to take thousands of Station logs of the company and predict an error corresponding to a log. This project wasn't so straight forward as it sounds. The complexity arises because of the presence of Non-Labelled raw data. A manual code was written to fetch messages from the logs. Topic modeling was applied to all the distinct messages and after that, they were grouped with the help of clustering algorithms. After grouping, different errors were identified and nomenclature for groups was done.
Finally, This problem reduced to supervised learning, and any message could be labeled denoting the type of error it produces.
FUTURE SCOPE:- Deep Learning could have been used to predict an error before it has arisen.
The following work after testing in stations could be implemented in Amazon Web Services

BusinessSoftware DevelopmentWritten CommunicationArtificial Intelligence (AI)Resolving IssuesPresentations

Aiesec

3 roles

Vice President

Jul 2019 – Sep 2019 · 2 mos · Tiruchirappalli, Tamil Nadu

BusinessTeachingWritten CommunicationPresentations

Team Leader

Jan 2019 – Jul 2019 · 6 mos · Tiruchirappalli, Tamil Nadu

TeachingWritten CommunicationPresentations

OGV Member

Jan 2018 – Jan 2019 · 1 yr · Tiruchirappalli, Tamil Nadu

TeachingWritten CommunicationPresentations

Indian institute of technology (banaras hindu university), varanasi

Research Intern

Nov 2018 – Jan 2019 · 2 mos · Varanasi, Uttar Pradesh

The project aimed at compartmentalizing tweets (Target- Kerala floods) into designated classes using Machine Learning Algorithms to speed up the rescue process during a calamity. Work included the construction of an Interface to automate the classification of raw data(tweets) into designated classes. Data pre-processing techniques like tokenization, stemming, removal of stop words, etc. were applied to the data to increase the efficiency of our model. Finally, conversion from textual data to Numeric vectors were completed. XGBoost, Sklearn and Random forest algorithms were applied to get the desired output

Written CommunicationCascading Style Sheets (CSS)Presentations