Smart AIOps for Incident Insights & RCA- 

• Led a 5-member team to design and deliver AIOps solutions for ServiceNow, enhancing analytics through Azure-based data pipelines, embeddings, and AI-powered search integrated with Microsoft Copilot. 

• Built an end-to-end system ingesting ServiceNow incident and change request data into Azure Blob Storage, creating embeddings and Azure AI Search indexes to enable keyword and semantic search, frequency analysis, and advanced date-based filtering. 

• Automated incident resolution recommendations by leveraging historical root cause analysis (RCA) documents, enabling the application to intelligently correlate new incidents with past patterns and suggest probable causes and remediation actions. 

• Drove 70%+ performance improvement in the AIOps360 ServiceNow Incident Intelligence platform through Azure AI Search hybrid indexing and LLM-powered RCA synthesis — reducing mean time to resolution for IT incident management. 

Multilingual AI Platform (text-to-audio & video-to-video Processing)- 

• Architected and owned end-to-end delivery of enterprise multilingual AI platform on Azure used by stakeholders across 80+ countries , including stakeholder management, development, and maintenance. 

• Led, built and enhanced full-fledged LLM-powered application supporting both text-to-audio (with accent preservation) and video-to-video transformation using Huggingface TTS, MarianMT models. 

• Developed a Streamlit-based application that analyzes text and associated audio files using LangDetect for language  detection and Whisper for transcription, identifying foreign words, and detecting mismatches between text and audio content. 

• Built a full-stack application using OpenAI API with Python, AWS S3, and MongoDB to generate phonemes and audio from user input words/languages, enabling users to select preferred phonemes to create a pronunciation library. 

Adaptive Risk Intelligence Platform- 

• Designed and developed an AI-driven risk assessment platform using adaptive, LLM-powered questionnaires to evaluate application risk across domains such as security, compliance, and operations. 

• Built a KPI-based risk scoring engine that normalizes multi-domain metrics and computes weighted risk scores, enabling consistent and explainable risk evaluation. 

• Implemented an automated reporting system generating structured risk reports with domain-wise analysis, key findings, and actionable recommendations. 

Document Translation Engine- 

• Architected a hybrid Document Translation Engine leveraging T5 for local, privacy-focused processing and OpenAI APIs for high-complexity linguistic tasks. 

• Developed a doc-to-doc translation pipeline that preserves original document structures, including layouts and tables, during the translation of sensitive enterprise files. 

• Engineered a local-first deployment to ensure 100% data security, eliminating the need for cloud storage while maintaining high-speed document automation. 

Agentic AI Workflow Hub-  

• Engineered a unified AI Orchestration Platform that integrates diverse multimodal applications—including text-to-audio, video-to-video, and doc-to-doc systems—into a single, cohesive interface. 

• Architected complex multi-step workflows using LangGraph and LangChain to facilitate seamless data handoffs between independent AI modules and specialized media processing tools. 

• Implemented MCP-based orchestration to standardize communication between disparate AI applications, enabling a modular and scalable hub-and-spoke architecture for cross-functional automation. 

AI Centre of Excellence (CoE)- 

• Established AI Governance frameworks and reusable component libraries reducing development redundancy by 40% and improving team productivity/efficiency by 25%. 

• Identified and prioritized AI initiatives, driving 2-3 times faster adoption of AI solutions across business units. 

Legacy Chatbots- Developed two types of chatbots: (1) legacy FAQ-based bots using SQL-backed knowledge; (2) modern RAG-based intelligent bots tailored for different customers and datasets. 

• Led the technical interview process for new hires, evaluating expertise in AI, NLP, and software development. 

• Built an AI-driven text generation solution leveraging NLP, Gemini LLM models, LangChain, AWS S3, OpenSearch and Generative AI techniques to extract key insights from historical text data of articles stored externally in a database using RAG and track trends based on keyword analysis which is integrated into a R Shiny app through APIs and deployed on posit connect. 

• Worked on database-based processes for gathering data, calculating prices and other related work structures by writing strong and automated formulas and methodologies in R and producing reports and data feeds. Perform proper data validation and quality assurance of every price and related process, formulas, and structures. 

• Migrated 4 data pipelines from Microsoft Excel to Oracle and R Shiny, improving automation, data quality, and maintainability across processes and products. 

• Developed production-grade, scalable R Shiny dashboards for various pricing processes. Followed best practices for debugging, unit testing, and quality checks, and deployed apps using Posit Connect. 

• Created reusable R packages using the golem structure to ensure a standardized and modular architecture across all R Shiny applications. 

• Performed data cleansing, data quality checking and management of data for product development processes, built predictive analytics and forecasting models and tools using regression, time-series analysis techniques, and statistical modeling to project future price scenarios, enhancing interpretability and supporting strategic pricing decisions on prices data and integrate the model in dashboards with Python and R Shiny, and optimized the models. 

• Built interactive data visualizations for possibility curves along with confidence levels the model has at different prediction levels, with percentage-based confidence scores. 

• Designed and implemented ML-based anomaly detection models to monitor mismatch of records between development, testing and production environments and ensure the integrity of price trends, users, traders, deals and other records reducing manual oversight and integrated with dashboards. 

• Developed and integrated automated predictive models into R Shiny dashboards, enabling real-time price forecasting and data-driven decision-making for editorial teams. 

• Support clients with queries relating to integrating Argus data and metadata into client systems. Prepare EDA tables and interactive plots with the help of plotly etc. to present to internal editorial teams and to internal and external clients. 

• Using project management tools, for example Jira, to manage all projects including new price creation, price creation, price codes, and many others. 

Responsibilities:

1. Analyzing clinical trial data to detect crucial and potential frauds using various statistical methods including demographic distribution, birthdate test, cluster analysis, perfect schedule of attendance, study visit, constant findings, duplicates records, multivariate outlier and inliers and adverse event summary with the help of JMP software and R Shiny applications.

2. Creating JMP- equivalent R Shiny dashboards for for statistical tests with modals, accuracy measuring parameters, logging, report generation functionalities.

3. Worked on integrating a natural language interface into R Shiny dashboards using LLMs, enabling users to query clinical trial data conversationally and receive dynamic visualizations based on statistical test results.

4. Being lead of the Statistical Monitoring team and QTL (Quality tailoring limit) team, my responsibilities include finalization of statistical monitoring plan and QTL plan, analyze the clinical trials data and assign and support team members to perform the quality tailoring check to detect crucial and potential frauds using SAS, R, R Shiny and JMP Clinical.

5. Based on the analysis, perform standard analysis to identify unusual or clustered patters that have the potential to impact the data integrity of the study and/or may potentially impact patient safety.

6. Creating Excel and HTML files using R-markdown scripts for adverse events (AE), SAE, BPD and OPD (Protocol Deviations) and make varieties of plots for visualization and comparison of different sites and their collected quality of data.

7. Made 2 R packages following golem structure using Bayesian methodology and Monte Carlo Simulation methods to compare currently available data with previously collected data at specified intervals of clinical studies to perform fraud detection and related analysis.

8. Used the teal and teal.logger R packages to implement logging and generate interactive reports, improving monitoring and reproducibility of data workflows.

9. Developed and validated ML-based models for pattern recognition in adverse event reporting, contributing to regulatory compliance and clinical safety insights and deployed on posit connect. 

10. To prepare methodologies, related documentation and do the modeling on SDTM datasets and convert the results to reports to process further for quality check.

11. Wrote unit tests using devtools, shinytest2 and testthat package and automated with GitHub Actions.

12. For updating, task assigning and record maintenance purpose, use project management tools like JIRA, Trello, spreadsheets.

13. Provide useful suggestions to improve and automate current process and involvement in development of new versions of current package and its testing to ensure its quality and standardization. 

14. Managed Table Listing Figures (TLFs) to organize and present data, automating reports with R to deliver accurate, timely data insights.

• To prepare standard research methodologies, decision-making techniques, and statistical methods for scraping and analyzing data to prepare research reports, case writings, and academic publications. 

• Conducted research and implemented machine learning algorithms such as classification, regression, clustering, time- series forecasting, natural language processing analysis, and survival models to support decision-making in academic studies. 

• Explored and adapted deep learning architectures and optimization strategies based on research to improve the performance of analytical solutions in operations research projects. 

• Extracted and preprocessed text from PDF-based research papers using web scraping, OCR, and NLP pipelines, and applied TF-IDF and frequency analysis to identify dominant terms within topic-specific corpora. 

• Built a logistic regression model on student admission data to predict enrollment likelihood, identifying key features influencing decision outcomes. 

• Applied a range of machine learning algorithms including logistic and linear regression, decision trees, random forest, XGBoost, SVM, KNN, PCA, and clustering techniques for predictive modeling, dimensionality reduction, and pattern recognition across diverse datasets. 

• To assist faculties in designing courses, identifying and collecting reading materials, developing teaching notes, grading class participation, quizzes, examinations, assignments, invigilation, etc. 

• Tools utilized- SPSS, R, R Shiny, GCP, Python, MS Office, Turnitin, Mendeley.