•  Design and Implementation: Designed and implemented over 30 technical solutions based on defined requirements and user stories from central Product Increment Planning, improving project delivery times by 25%.
• CI/CD Automations: Developed CI/CD automations for 10 key projects using Docker and Kubernetes, achieving a 50% faster deployment cycle and reducing manual intervention by 40%.
• Cloud Provisioning: Automated Azure provisioning with Terraform for 5 biotech projects, enhancing resource efficiency by 30% and reducing setup time from 10 days to 3 days.
• Machine Learning Models: Developed and deployed 4 Python-based machine learning models for medical diagnostics on Azure, improving classification accuracy by 20% and reducing processing time by 35%.
• Pair Programming and Testing: Implemented solutions in pair programming mode, conducting over 100 extensive tests to ensure code quality and functionality.
• Documentation: Created comprehensive documentation for over 20 technical solutions, ensuring knowledge transfer and facilitating maintenance, which reduced onboarding time for new team members by 50%.
• Technical Consulting: Consulted with Product Owners, Architects, and Stakeholders on 15 projects to refine solution concepts, leading to a 30% increase in project alignment and satisfaction.
• Quality Focus: Ensured the quality, scalability, and maintainability of implemented solutions, resulting in a 40% reduction in post-deployment issues and a 25% increase in system reliability.

• Containerized a Flask web app with Docker, bundling the app along with over 15 dependencies to ensure consistent execution across environments
• Automate the provisioning of 8 essential Azure resources, including networking components and an AKS cluster, employing Terraform, creating a flexible IaC shell framework adaptable to various deployment scenarios
• Defined Kubernetes scalable deployments with 3 replicas, enabling high availability across multiple environments
• Developed a rolling update strategy in Kubernetes for seamless application updates
• Improved deployment efficiency by 50% with refined Azure DevOps CI/CD pipelines, enhancing the team's ability to manage releases in a multi-environment setup
• Established Azure Monitor strategy, tracking over 10 critical application metrics to ensure optimal performance and reliability in each environment post-deployment

• The Challenge: Tasked with enhancing a medical search engine, the challenge was to process unstructured biomedical literature and deploy an intelligent model on Azure Databricks. The goal was to improve the retrieval of information closely related to specific medical queries, leveraging my background in biomedicine and bioinformatics
• My Approach : Utilizing Python and interdisciplinary knowledge in biomedicine and bioinformatics, I developed an NLP pipeline with Azure's text analytics cognitive service. This involved cleaning textual data and implementing word embeddings to capture contextual relationships within the medical field.
  
• The Solution: A machine learning model was deployed on Azure Databricks, employing NLP techniques to understand complex medical terminologies. The model provided a backend for the search engine, enabling it to deliver precise and contextually relevant results, improving the efficiency of medical research.
  
• The Outcome: The project significantly boosted the search engine's functionality, increasing result accuracy by 40% and reducing healthcare professionals' search time by 30%, thus becoming crucial for clinical decisions and research.
  
• My Contribution: I preprocessed data, trained the model to detect patterns in medical terms, and deployed the solution using Azure services. My work enhanced the search engine's term-matching capabilities by 50%, improving the user experience for medical professionals. Additionally, I containerized the Streamlit user interface with Docker, ensuring a smooth deployment on Azure App Services.
  

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• The Challenge: Faced with the intricate task of classifying genetic mutations for personalized cancer treatment, the project required a precise analysis of medical literature. The objective was to automate the classification process, which traditionally involved time-consuming manual reviews by clinical pathologists.
• My Approach: My approach capitalized on my Python programming skills and understanding of genomics to preprocess textual data from medical literature. I employed natural language processing techniques such as lemmatization and tokenization to structure the data effectively. Utilizing "Tfidf Vectorizer," I extracted features that captured the relationships between words, crucial for the subsequent machine learning phase.
  
• The Solution: Building on the preprocessed data, I developed a composite machine learning model that integrated Logistic Regression, KNN, Random Forest, and Naive Bayes algorithms. This multifaceted model harnessed the unique capabilities of each algorithm to accurately classify genetic mutations, crucial for advancing personalized cancer treatment.
  
• The Outcome: The deployment of this machine learning model streamlined the mutation classification process, reducing the time pathologists spent on manual reviews by approximately 70%. It accelerated the identification of driver mutations, facilitating faster and more personalized treatment decisions for cancer patients.
  
• My Contribution: I was instrumental in the end-to-end development of the predictive model, overseeing data splits and fine-tuning hyperparameters. My efforts improved model accuracy by 40%, halved processing time, and reduced computational demands while ensuring precise mutation classification.

• Conducted graph-based computational analysis to study the spatial and structural organization of prostate tumors from histological slide images
• Employed Python's scientific libraries (Numpy, Matplotlib, Pandas, NetworkX) to develop algorithms for processing and interpreting complex biological data
• Programmed and executed code for reading image tiles, applying masks, stitching together image segments, and performing geospatial graph-based algorithms on tumor cell maps
• Advanced prostate cancer research by employing Python and graph theory to analyze histological images, contributing to the development of novel tumor analysis methods

• Contributed to a deeper understanding of tumor cell biology by enhancing the precision of digital pathology analyses
• Supported the research team in developing innovative approaches to visualize and quantify the tumor microenvironment, aiding ongoing cancer research efforts

• The Challenge: To build a data processing system for the NHS capable of managing and analyzing extensive COVID-19 data from Johns Hopkins University, providing public health officials with the insights needed to respond to the pandemic.
• My Approach: I developed an automated system on AWS to process and analyze COVID-19 data efficiently. This involved using AWS Lambda to handle data as it arrived, AWS Glue to organize the data for analysis, and AWS S3 for secure storage. I set up Amazon Redshift for our database needs, which allowed us to run complex searches quickly, and used Amazon QuickSight to create user-friendly dashboards that presented the data clearly to decision-makers.
• The Solution: I created a robust AWS analytics platform that quickly processed COVID-19 data, using Amazon Redshift for fast query execution and Amazon QuickSight for interactive dashboards, giving officials real-time insights for informed decision-making.
• The Outcome: The deployment of this analytics platform significantly enhanced public health response capabilities, improving the decision-making process by 50%. It enabled a more agile approach to strategizing interventions and effectively allocating resources where most needed.
• My Contribution: Played a crucial role in automating the data pipelines, which increased analysis speed by 35%. Managed cloud resources to ensure platform availability and used PySpark for efficient data transformations. Orchestrated workflows with Amazon EventBridge, delivering consistent, updated insights.

• Orchestrated MS Access to Azure SQL migration with PowerShell, revamping data systems and simplifying BI reporting via a Django front-end
• Enhanced internal workflows by automating 40% of manual BI tasks, significantly easing report generation and data entry processes for sales teams and departments