I am a Data Scientist and Cloud ML Engineer with strong experience in building end-to-end machine learning pipelines, GenAI solutions, and cloud-native automation using Microsoft Azure and AWS. My work spans across ML system design, scalable data engineering, LLM applications, and production-grade observability.

I specialize in integrating Data Science + Cloud + MLOps + GenAI to deliver high-impact solutions across industries such as Sales Intelligence, Life Sciences, Healthcare, and Analytics Engineering.

Python • SQL •

Pandas • NumPy • Scikit-learn • XGBoost • Matplotlib • Seaborn

Azure Machine Learning • Azure AI Foundry • Azure OpenAI • Azure Storage • Azure Functions • Azure DevOps

SageMaker • Lambda • EC2 • S3 • CloudWatch • ECR

GitHub Actions • MLflow • Docker

Streamlit • Power BI

• Supervised & unsupervised ML models
• Feature engineering & optimization
• Ensemble modeling & model tuning
• LLM applications using Azure OpenAI
• RAG-based chatbot development
• Document summarization & structured extraction

• Azure Machine Learning: pipelines, compute clusters, deployments
• Azure AI Foundry: flow development, custom copilots, evaluation
• Azure OpenAI: GPT-based assistants, embeddings, content generation
• AWS SageMaker: training jobs, model registry, batch inference
• Serverless ML using Lambda, EventBridge, API Gateway

• CI/CD for ML (GitHub Actions, Azure DevOps)
• Model versioning, lineage, and registry
• Model drift detection & performance dashboards
• Real-time alerting & automated retraining loops
• Monitoring inference latency, accuracy, data shifts

• Cancer prediction ML models
• Patient report summarization using LLMs
• Visual analytics dashboards for clinical workflows

• Customer segmentation using SQL and Power BI to understand buying patterns and behavior
• Product performance analysis across categories to identify sales leaders and underperforming SKUs
• Top-selling product insights: using SQL aggregations and Power BI dashboards to highlight the most-sold products from a wide product portfolio
  • Monthly/weekly sales trend comparison
  • Revenue contribution breakdown
  • Profitability insights and SKU-wise performance
  • Seasonality and demand spikes
• Promotional strategy support: showcasing key product features, customer value propositions, and sales drivers to boost marketing ROI
• Visualization using Power BI, SQL queries, and Python (Matplotlib/Seaborn/Plotly) for deeper analytics
• Actionable dashboards for leadership: category filters, trend lines, heatmaps, contribution charts

• Sales intelligence copilots (lead scoring, email automation, opportunity insights)
• Healthcare report summarization & clinical assistant agents
• Data exploration chat agents for BI teams
• Custom LLM evaluation frameworks

• DP-100 Azure Data Scientist Associate (in progress)
• MLS-C01 AWS Machine Learning Speciality (target)
• Continuous learning in GenAI, MLOps & LLM agent development

A full-stack project performing EDA, visualization & insights extraction for asteroid close approaches.
Includes filters, interactive dashboards & SQL analytics.
Tech: Python, SQL, Streamlit, Plotly, Matplotlib, VS Code