Software | Cloud | AI

Shutonu Mitra

I am a backend-leaning Software Engineer with experience building scalable APIs, cloud-native services, and reliable backend systems in production environments.

I focus on applied AI, integrating machine learning into real-world software through NLP-driven services, graph-based modeling, and data-intensive platforms.

Email meGitHubResume

Interests

Backend Engineering

I design and build scalable APIs, microservices, and backend architectures with a focus on performance, fault tolerance, and long-term maintainability.

Cloud & Distributed Systems

Experience building and operating cloud-native systems using containerization, managed services, and distributed data pipelines on modern cloud platforms.

Applied AI

I focus on integrating AI and LLM-based features into production systems, emphasizing observability, failure handling, and real-world user impact.

Machine Learning

Experience developing and deploying ML and NLP-driven services with attention to evaluation quality, scalability, and maintainable model workflows.

Education & Experience

Education

  • Software Engineering
  • Distributed Systems
  • Operating Systems
  • Cloud Computing & Applications
  • Big Data Technologies
  • Machine Learning I & II
  • Fundamentals of Information Security
  • Web & Mobile Application Development

Professional Experience

  • Developed an Azure-deployed C#/.NET backend and Angular (TypeScript) internal portal to centralize hiring-to-onboarding tracking across Greenhouse, WinTeam, and Checkr for HR and operations.
  • Implemented cross-system integrations from Greenhouse (GHO) into WinTeam using .NET and T-SQL, mapping recruiting and onboarding entities into ERP schemas; integrated Checkr background-check and SSN verification statuses with validation and state propagation.
  • Debugged cross-system integration failures by tracing requests and workflow state transitions and validating SQL Server data to identify schema and mapping mismatches, restoring reliable onboarding processing.

Skills

Technologies I work with across backend, cloud, and applied AI.

Languages

PythonLanguages
TypeScriptLanguages
JavaLanguages
C# / .NETLanguages

Cloud

AWSCloud
GCPCloud
AzureCloud
DockerCloud
KubernetesCloud

Backend

FastAPIBackend
Spring BootBackend

Data

PostgreSQLData
MySQLData
SparkData
AirflowData

Frontend

ReactFrontend
AngularFrontend

AI/ML

LLM / RAGAI/ML
PyTorchAI/ML
SageMakerAI/ML

Projects

Featured

Job AI Copilot

PythonLLMAWS LambdaFastAPI

Job Application AI Copilot is a production-focused AI platform that helps candidates tailor resumes and reason about job fit through explainable, role-specific feedback. It uses agentic AI pipelines to parse job descriptions, analyze resumes, and surface skill gaps while preserving factual accuracy. The system is built on stateless APIs and serverless infrastructure to support concurrent usage and burst workloads. AI components are treated as modular system parts with bounded reasoning, prompt versioning, and failure-aware workflows.

Code

Paper to Code

PythonLLMPDF ProcessingResearch Tools

Paper to Code is an AI-powered tool that converts algorithm descriptions from research papers into executable code using structured LLM reasoning. It ingests PDFs, extracts relevant algorithmic content, and translates it into clear, runnable implementations. The system treats LLMs as reasoning engines, preserving algorithmic intent while avoiding unsupported or hallucinated steps.

Code

Predictis

IoTMachine LearningMobileHealthcare

Predictis is a system consisting of a wearable device and mobile application, which allows users to know their risk levels of having CVDs in the future. IoT and ML techniques classify users into risk levels with an F1 score of 80.4% (three-class) and 91% (binary). The stacking classifier incorporating best performing ML algorithms was used for predicting risk levels utilizing the UCI Repository dataset.

Code

Neural-Network Design

PythonNeural NetworksTime SeriesARMA

A Python-based repository focused on key Machine Learning and Time Series Analysis algorithms. It includes implementations of ARMA model simulations, neural network training algorithms such as backpropagation, and hybrid models like Radial Basis Function (RBF) networks with backpropagation.

Code

AWS Lambda Functions

AWS LambdaAPI GatewayRDSMySQL

AWS Lambda functions serving as the backend for a MySQL-supported web application. Functions handle REST API endpoints for fetching, adding, and managing categories and books. All APIs are exposed via Amazon API Gateway with the database hosted on AWS RDS.

Code

Opinion Mining of Tweets Related to Mental Health

PythonLSTMNLPSentiment Analysis

An EDA project reflecting public opinion related to common mental health problems through time-series analysis from collected Twitter data. Data annotated using VADER into positive, negative, and neutral classes. LSTM architecture achieves 85% accuracy.

Code

Body Signal Analysis of Smokers and Drinkers

PythonMachine LearningClassificationClustering

Thorough analysis of machine learning methodologies applied to the Smoking and Drinking Dataset with Body Signal. The project delves into regression analysis, classification, clustering, and association rule mining.

Code

Malicious URL Detection

PythonMachine LearningSecurityCNN

Utilizing machine learning algorithms for detection and prevention of malicious URLs. Development and evaluation of Decision Trees, Random Forest, Adaboost, KNN, SGD, ExtraTree, SVC, Gaussian NB, and 1D-CNN for identifying malicious URLs.

Code

PPD Coach

AndroidHealthcare3D AnimationVisual Questionnaires

An application for detecting Postpartum Depression in Bangladeshi Mothers using Visual Questionnaires. Scenario-based visual representation of EPDS questionnaires using 3D animation videos integrated into an Android application.

Code

PaperTown

ReactTypeScriptMySQLJava

A Bookstore web application using a React client app and a Tomcat server with a MySQL database. Single page architecture at client built with React in TypeScript and monolith server architecture having Restful API.

Code

ASMA

PHPOracleJavaScriptHTML

A system for managing a hair salon. Keeps track of employee salaries, inventory and customer data along with billing. Fully equipped system managing the entire functioning of a hair salon.

Code

SeekNShare

HTMLCSSJavaScriptFirebase

A website built with HTML, CSS and JavaScript with Firebase used as backend.

Code

JAM KOM – A Deep Learning Based Traffic Update System

Deep LearningMobileTraffic Analysis

Creating a mobile application called Best Route Analyzer that gives the best route with minimum waiting time after the source and destination area are given.

Gas Leakage Detector

IoTEmbeddedSMS Alerts

An IoT based system capable of detecting gas leaks and sending alert messages via SMS to the user's cellphone as well as triggering an alarm.

Code

Graphics – 3D & 2D

OpenGLBlenderC/C++Animation

Two graphics projects with animations. OpenGL (C & C++) for 2D graphics. 3D graphics project done in Blender with modelling, texturing, shading, rigging, and animations using multiple light sources and camera angles.

A Game of Cyphers

CCryptographyAlgorithms

A program in C applying popular encryption algorithms to encrypt the given message.

Code

Research & Publication

As a Graduate Research Assistant, I collaborated with faculty to extract high-quality data from social media platforms for detecting cyber fraud cascades. I played a key role in defining social cyber vulnerability metrics, which led to a 25% improvement in social scam classification accuracy. I developed advanced multimodal data analytics and algorithms, utilizing Graph Neural Networks and Reinforcement Learning, while applying cutting-edge NLP and deep learning techniques to analyze unstructured text data, ultimately reducing false positives by 20%. Additionally, I contributed to the creation of a geospatial dashboard that maps the Social Cyber Vulnerability Index (SCVI) across different regions.

Shutonu Mitra, Qi Zhang, Hemant Purohit, Chang-Tien Lu, and Jin-Hee Cho, "Towards Inclusive Cybersecurity: Protecting the Vulnerable with Social Cyber Vulnerability Metrics," The Sixth IEEE International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications, 2024. (Accepted)
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Ping me anytime

Open to backend, cloud, and applied AI opportunities.

shutonumitra@gmail.com
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