Vaishnavi Ainapure

Software Engineer

Mumbai, Maharashtra, India3 yrs 7 mos experience

Most Likely To SwitchAI ML Practitioner

Key Highlights

Developed RAG pipeline reducing ticket resolution time.
Created FullStack MERN application for energy management.
Published research on ML techniques in quality assurance.

Stackforce AI infers this person is a Full-Stack Developer with expertise in AI/ML applications in SaaS and research environments.

Contact

Skills

Core Skills

Software DevelopmentMachine LearningBusiness AnalysisCommunication

Other Skills

KubernetesAWS LambdaMachine Learning AlgorithmsWeb Application DevelopmentCloud ComputingCI/CDGitHub ActionsPHPMERNProblem SolvingTechnology TrendsData AnnotationBootstrapContinuous ImprovementProgramming Languages

About

My endeavour is to consistently upgrade my knowledge, skill set and utilize it for profitable growth of the organization.

Experience

3 yrs 7 mos

Total Experience

1 yr 9 mos

Average Tenure

2 yrs 11 mos

Current Experience

Schneider electric

Software Engineer

Jul 2023 – Present · 2 yrs 11 mos

Contributing to strategy and implementation projects involving LLMs, RAGs intelligent automation, and
responsible AI adoption.
Developed a RAG pipeline using OpenAI and reducing internal ticket resolution time
Conducted technical code reviews and mentored 3 junior engineers, improving team deployment frequency by 15%
through standardized CI/CD practices
Developed Automated Code Resolution-Multi-Agent Framework
Developed and Integrated GitHub Copilot powered Automated Code Reviews in Enterprise Code Repositories
Identified and resolved cloud resource leaks in Azure, saving the department Rs. 12,000/month in infrastructure
overhead.
Developed FullStack MERN Web application of an Energy Management System which is deployed on Azure Cloud,
delivering efficient energy monitoring and analytics.
Implemented CI/CD pipelines to enable seamless deployment and continuous integration in Azure and across
various environments. Implemented automated static code analysis and binary scan processes across entire
organizational divisions using GitHub Actions to enhance code quality and security. Migrated organizational code
repositories from Subversion (SVN) to GitHub, streamlining version control and collaboration workflows.
Built an end-to-end web application using PHP for motor and relay control, monitoring and analysis-ensuring
robust performance and maintainability optimizing response time by 95% compared to earlier legacy application integrated with lighttpd and custom linux kernel

KubernetesAWS LambdaMachine Learning AlgorithmsWeb Application DevelopmentCloud ComputingCI/CD+4

Idfc first bank

Associate Analyst

Oct 2022 – Jun 2023 · 8 mos · Mumbai, Maharashtra, India · Hybrid

Software DevelopmentBusiness AnalysisProblem Solving

The sparks foundation

Intern

Jun 2021 – Nov 2021 · 5 mos

Software Development

Simyu renewable pvt ltd

Intern

Jun 2021 – Aug 2021 · 2 mos

Tata institute of fundamental research

Intern

Mar 2021 – May 2023 · 2 yrs 2 mos · Mumbai, Maharashtra, India · On-site

Research paper-Springer Conference -2022 ICSADL Proceedings --Sr.No. 57.Deep-learning based quality assurance of silicon detectors in Compact Muon Solenoid experiment link:https://link.springer.com/chapter/10.1007/978-981-19-5443-6_56
https://drive.google.com/file/d/1McEYG1rYCYxJvACtdVJ2BeZRV8Y0fDR0/view
This project involved interaction with team in Texas Tech University
In the Compact Muon Solenoid (CMS) experiment at CERN, Geneva, a large number of HGCAL sensor modules are required. There are about 25000 modules to be fabricated and each sensor module contains about 700 checkpoints for visual inspection. This project proposes ML based techniques so that the process of inspection, defect detection and quality control is automated.
Provided data was annotated and used for training machine learning models(explored machine learning models include yolov4, scaled yolov4 and yolov4 tiny)
Research paper-Springer Conference -2022 ICSADL Proceedings.This project involved interaction with team in Texas Tech University -In the Compact Muon Solenoid (CMS) experiment at CERN, Geneva, a large number of HGCAL sensor modules are required. There are about 25000 modules to be fabricated and each sensor module contains about 700 checkpoints for visual inspection. This project proposes ML based techniques so that the process of inspection, defect detection and quality control is automated. -Provided data was annotated and used for training machine learning models(explored machine learning models include yolov4, scaled yolov4 and yolov4 tiny)
Skills: Technology Trends · Machine Learning Algorithms

Technology TrendsMachine Learning AlgorithmsMachine Learning