SS
Open to AI/ML Engineering & Research Internships

AI & Machine Learning Engineer

I build practical machine learning and deep-learning applications, including predictive models, medical image segmentation, and speech emotion recognition. My work focuses on Python, model evaluation, and turning experiments into useful applications.

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Technical Focus

Programming & Data

PythonSQLPandasNumPyData Preprocessing

Machine Learning

XGBoostCatBoostLightGBMFeature EngineeringTime Series

Deep Learning

PyTorchTensorFlow3D U-NetEfficientNetImage Segmentation

Speech AI

LibrosaMFCCCNNSpeech Emotion RecognitionModel Evaluation

Selected Work

View all 7 projects
SYSTEM ARCHITECTUREDistributed AI research orchestrationResearch QuestionProblem intakeResearch PlannerQuestion-aware planLiterature RetrievalOpenAlex / Semantic ScholarResearch AgentsAnalyze evidenceExperiment PlannerMetrics + training planDistributed WorkersDMLF / PyTorch DDPResearch BriefEvidence + resultsOffline-demo mode uses deterministic agents and simulated workers where external services are unavailable.

Aster — Distributed AI Research Scientist

Built a hackathon-stage research orchestration MVP that turns an ML question into a literature brief, falsifiable hypothesis, bounded experiment plan, worker execution, analysis, and an evidence-labelled Markdown report.

Node.jsLLM APIsMulti-Agent SystemsPyTorch DDPgRPCDockerExperiment Orchestration

Engineering Highlights

  • Kept research plans bounded and added a critic step with explicit caveats and human-approval requirements
  • Integrated an asynchronous worker protocol with job submission, status polling, progress updates, and metric collection
  • Connected the orchestration layer to the separate Distributed ML Training Framework
  • Research orchestration needs explicit review gates and inspectable intermediate artifacts
  • Distributed execution and scientific evidence are separate concerns
  • Run manifests and clear simulated-versus-real labels make results easier to audit
View Case Study
SYSTEM ARCHITECTURELarge-scale city-specific ML forecastingHistorical Weather90 GB datasetPreprocessingCleaning + feature engineeringCity-Specific ModelsThousands of locationsXGBoostPer-city forecastingFastAPIPrediction servingForecast OutputTemperature / weather predictionsCity-specific XGBoost models are exposed through an API-oriented serving layer.

Weather Prediction System

Built a city-specific weather prediction pipeline using XGBoost and historical weather observations, with preprocessing, lag-based feature engineering, and an application interface for accessing predictions.

PythonXGBoostPandasTime SeriesFastAPIDockerGradio

Engineering Highlights

  • Prepared historical weather observations for modeling
  • Designed lag-based features for time-dependent prediction
  • Organized training around city-specific models
  • Feature engineering and temporal validation are important for forecasting
  • Location-specific models can capture local patterns
  • A prediction interface helps make model outputs accessible
View Case Study
SYSTEM ARCHITECTURE3D U-Net → EfficientNet → Attention pipelineBraTS 20203D MRI volumesPreprocessingNormalization + patchesBaseline 3D U-NetDice 0.8057EfficientNet U-NetDice 0.8157Attention + EfficientNetDice 0.8256ValidationDice + segmentation metricsBest validation Dice: 0.8256 using the Attention + EfficientNet architecture.

Brain Tumor Segmentation

Investigated 3D deep-learning architectures for brain tumor segmentation using multimodal MRI data from the BraTS dataset.

PythonPyTorch3D U-NetAttention U-NetEfficientNetMONAI

Engineering Highlights

  • Prepared and normalized volumetric MRI data
  • Compared baseline and attention-enhanced 3D segmentation architectures
  • Managed GPU memory constraints during patch-based training
  • Three-dimensional models require careful memory and patch-size management
  • Architecture comparisons should use consistent validation methodology
  • Dice and IoU provide complementary views of segmentation quality
View Case Study

Research & Publications

Speech Emotion Recognition

Research on speech emotion recognition using data augmentation and a lightweight convolutional neural network.

Speech AICNNMFCCAudio Processing
Publication

Speech Emotion Recognition: A Human-Centric Framework with Enhanced Data Augmentation and Lightweight CNN

IEEE DECoN 2025

DOI: 10.1109/DECoN67170.2025.11448083

Medical Image Segmentation

Experiments with 3D deep-learning architectures for brain MRI tumor segmentation using the BraTS dataset.

PyTorch3D U-NetEfficientNetBraTS

Weather Prediction

City-specific weather prediction using historical observations, lag-based features, and XGBoost.

PythonXGBoostTime SeriesFeature Engineering

Experience

2025

AI Research Intern

Coding Jr

  • Conducted AI market research across 50+ industry participants and synthesized findings into structured competitive analysis.
  • Prepared 10+ presentations communicating research findings, technology trends, and competitive insights.
2026

Data Analysis and LLM Intern

Springer Capital

  • Contribute to data analysis and business insight generation.
  • Support LLM-related work and AI evaluation workflow improvements.
2025–Present

Independent ML Projects

Personal Research and Development

  • Developed projects in weather prediction, brain MRI tumor segmentation, speech emotion recognition, and traffic demand prediction.
  • Applied model evaluation, feature engineering, data preprocessing, and reproducible experimentation across machine learning workflows.

Technical Stack

Programming

PythonSQLJava (Basic)

Machine Learning

XGBoostCatBoostLightGBMFeature EngineeringTime Series

Deep Learning

PyTorchTensorFlowCNN3D U-NetEfficientNet

Audio & Medical AI

LibrosaMFCCBraTSMONAIImage Segmentation

Data & Evaluation

PandasNumPyScikit-learnExploratory Data AnalysisModel Evaluation

Deployment & Tools

FastAPIDockerGradioSQLiteWSL

By The Numbers

90 GB

Largest Dataset Processed

Handled end-to-end in training and evaluation pipelines

4,300+

Cities Modeled

City-specific weather forecasting across a decade of data

7

Engineering Case Studies

Distributed systems, agentic AI, ML, and research projects

1

IEEE Publication

Peer-reviewed research in speech emotion recognition

GitHub Contribution Graph

Let's build something.

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