Srishti Adkar - Software Engineer & UX Designer

About Me

I'm proficient in Python, SQL, and Java, and skilled in using frameworks like Scikit-learn, TensorFlow, and XGBoost. I work comfortably with cloud platforms such as AWS, Azure, and GCP, and have hands-on experience with tools like Docker, REST APIs, and data visualization dashboards.

Whether it's crafting clean code, automating data pipelines, or solving complex problems with machine learning, I'm driven by curiosity, creativity, and the impact of technology. Let's build something amazing together!

Full Stack Development

Machine Learning

Data Engineering

UI/UX Design

Work Experience

AI/ML Engineer Intern

HSBC

Worked on development of an advanced Claims Fraud Detection System, collaborating with cross-functional teams to gather requirements and define key fraud detection metrics, aligning closely with business objectives to achieve a 25% gain in fraud detection accuracy.
Aligned with data engineering teams to design and used a robust ETL pipeline using Python, SQL, and AWS tools, automating data extraction and preprocessing from diverse sources, which led to a 40% progress in data processing efficiency and timeliness.
Developed and trained a fraud detection model utilizing historical claims data (e.g., claim amounts, claimant history, claim frequency) with machine learning techniques, including Random Forest and XGBoost, resulting in a 30% reduction in fraud detection errors and higher operational reliability.

Nov 2024 – Present

Teaching Assistant

Seattle University

Assisted students with core C++ programming concepts, including data structures, algorithms, and debugging techniques.
Guided students through programming assignments, provided one-on-one support, and conducted review sessions to strengthen problem-solving skills.
Created supplementary learning materials and contributed to assignment grading and feedback.
Supported students in configuring and managing AWS EC2 instances, teaching cloud infrastructure best practices.
Facilitated lab sessions involving Docker and AWS, helping students complete hands-on projects in big data environments.

Mar 2024 – Jun 2024

Jun 2024 – Sep 2024

Software Engineer Intern

St. Francis House

Developed data pipelines for automated data ingestion and preprocessing, leveraging Python and SQL to manage large-scale datasets on AWS and GCP.
Designed SQL queries for sub-second latency and integrated them into REST APIs for high-traffic production environments.
Automated data migration between AWS and GCP, ensuring zero data loss by applying data warehousing best practices.
Implemented predictive analytics models (Random Forest, XGBoost) to optimize data workflows and improve processing efficiency.

ML Engineer

Atomic Loops Pvt Ltd

Collaborated with cross-functional teams to define business objectives and deliver accurate audio classification models for the Audio Analysis PoC Project.
Automated ETL pipelines for large-scale audio data using Python, Pandas, and Azure Data Factory, ensuring efficient data integration and storage.
Developed and tuned ML models (CNN, RNN, XGBoost) for audio analysis, improving event detection and classification accuracy.
Created interactive dashboards for audio feature analysis and predictions, enabling actionable insights for stakeholders.

Oct 2021 – Aug 2023

Portfolio

Featured Projects

RetinaFace – A Face Detection Tool

Developed an advanced face detection system using the RetinaFace architecture, leveraging deep learning techniques to achieve high accuracy and real-time performance. Implemented the model using Python and PyTorch, and optimized it for robust detection across diverse datasets. Integrated the solution into a scalable pipeline for automated image analysis and reporting.

Computer Vision Deep Learning PyTorch Python Face Detection

SUMAZON – Seattle University's Campus Store Website

Developed a full-stack e-commerce web application for Seattle University's campus store, designed to streamline product browsing, purchasing, and inventory management. Utilized a three-tier architecture with React for the frontend, Django for the backend, and PL/SQL for database operations. Implemented secure authentication, order processing, and an intuitive user interface to enhance the campus shopping experience.

React Django PL/SQL Full Stack E-commerce

SpendSmart – Budget Management

Developed a budget management application tailored for international students, enabling users to efficiently track expenses, set savings goals, and manage monthly budgets. Built with the MERN stack, the platform features secure authentication, real-time data visualization, and seamless integration with Azure SSO. Designed intuitive dashboards and implemented robust backend logic to ensure a smooth and insightful user experience.

MERN Stack Azure SSO Budgeting Data Visualization

HandyRent – Equipment Rental

Designed and implemented an online marketplace for renting rarely used equipment, streamlining the process for both owners and renters. Utilized ASP.NET for the backend and Azure for cloud hosting, ensuring scalability and security. Integrated advanced search, booking management, and payment processing features, and developed a user-friendly interface to enhance the rental experience for all users.

ASP.NET Azure JSON Marketplace Cloud

Diamond Data Analysis and Modeling

Conducted a comprehensive machine learning project to analyze and predict diamond prices, classify diamond types, and group similar diamonds. Implemented regression, clustering, and classification techniques using Python and Flask.

Machine Learning Data Analysis Python Flask Regression Classification Clustering

Retrieval-Augmented Generation (RAG) System for Document-Based QA

Built a Retrieval-Augmented Generation (RAG) system to enable context-aware question answering over custom PDF and text documents. Leveraged LangChain for document loading, text splitting, and embedding using OpenAI.

RAG LangChain OpenAI ChromaDB NLP GenAI

Hi, I'm Srishti Adkar