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Hi, I’m Shivani Murukannaiah—a data scientist and generative AI engineer who thrives on turning complex data into practical insights. Over the past three years, I’ve built ML, NLP, and deep learning models, including large language models, and I’ve designed cloud-scale data pipelines that scale. I enjoy collaborating with researchers and business teams to translate data into actionable decisions, optimize model performance, and deliver AI-powered applications and RAG-enabled knowledge systems. My toolkit includes Python, PyTorch, TensorFlow, AWS, SQL, and a passion for turning ideas into reliable, scalable solutions.…Hi, I’m Shivani Murukannaiah—a data scientist and generative AI engineer who thrives on turning complex data into practical insights. Over the past three years, I’ve built ML, NLP, and deep learning models, including large language models, and I’ve designed cloud-scale data pipelines that scale. I enjoy collaborating with researchers and business teams to translate data into actionable decisions, optimize model performance, and deliver AI-powered applications and RAG-enabled knowledge systems. My toolkit includes Python, PyTorch, TensorFlow, AWS, SQL, and a passion for turning ideas into reliable, scalable solutions.

Shivani Murukannaiah

Data Scientist, AI Engineer, Data Analyst, +2

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Hi, I’m Shivani Murukannaiah—a data scientist and generative AI engineer who thrives on turning complex data into practical insights. Over the past three years, I’ve built ML, NLP, and deep learning models, including large language models, and I’ve designed cloud-scale data pipelines that scale. I enjoy collaborating with researchers and business teams to translate data into actionable decisions, optimize model performance, and deliver AI-powered applications and RAG-enabled knowledge systems. My toolkit includes Python, PyTorch, TensorFlow, AWS, SQL, and a passion for turning ideas into reliable, scalable solutions.…Hi, I’m Shivani Murukannaiah—a data scientist and generative AI engineer who thrives on turning complex data into practical insights. Over the past three years, I’ve built ML, NLP, and deep learning models, including large language models, and I’ve designed cloud-scale data pipelines that scale. I enjoy collaborating with researchers and business teams to translate data into actionable decisions, optimize model performance, and deliver AI-powered applications and RAG-enabled knowledge systems. My toolkit includes Python, PyTorch, TensorFlow, AWS, SQL, and a passion for turning ideas into reliable, scalable solutions.

Available to hire

Hi, I’m Shivani Murukannaiah—a data scientist and generative AI engineer who thrives on turning complex data into practical insights. Over the past three years, I’ve built ML, NLP, and deep learning models, including large language models, and I’ve designed cloud-scale data pipelines that scale.

I enjoy collaborating with researchers and business teams to translate data into actionable decisions, optimize model performance, and deliver AI-powered applications and RAG-enabled knowledge systems. My toolkit includes Python, PyTorch, TensorFlow, AWS, SQL, and a passion for turning ideas into reliable, scalable solutions.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Work Experience

Research Data Analyst – Machine Learning and Genomics at UConn Health

June 1, 2025 - Present

Engineered end-to-end ML and bioinformatics pipelines on AWS and HPC (Xanadu) to process 750GB+ RNA-seq data, reducing overall workflow time by 40% through optimized QC and parallel execution. Built tumor-risk prediction models using Logistic Regression, Random Forest, and XGBoost, improving high-risk tumor identification by 27% and enabling more accurate clinical decision-making. Developed production-ready genomics workflows including alignment, normalization, DESeq2 differential expression, batch-effect correction, and feature engineering to improve model robustness and reproducibility. Designed interactive Power BI and Tableau dashboards to visualize DE genes, enrichment pathways, and clinical metadata, accelerating research analysis for oncology teams. Implemented HIPAA-compliant, cloud-native genomics pipelines using AWS S3, EC2, Lambda, and Step Functions, ensuring secure, scalable, and reproducible data processing for NIH and cross-institution collaborations.

AI Engineer at MasterCard

January 1, 2025 - May 31, 2025

Developed and deployed LLM-powered AI assistants using GPT-4, LangChain, and Hugging Face, automating internal workflows and improving response quality for operations teams. Built RAG pipelines with Pinecone and OpenAI embeddings, increasing document retrieval accuracy by 40% and enhancing knowledge search for compliance and mortgage teams. Designed and optimized FastAPI/Flask microservices for production AI model serving, enabling low-latency inference and seamless integration with enterprise systems.

Data Analyst at Accenture

May 1, 2022 - May 31, 2024

Analyzed complex business datasets using SQL, Python, and Power BI to generate actionable insights that improved operational efficiency by 25%. Built automated dashboards and KPI trackers, reducing manual reporting time by 40% and enabling data-driven decision-making across cross-functional teams. Collaborated with stakeholders to define data requirements, conducted root-cause analysis, and delivered recommendations that optimized client workflows and reduced process bottlenecks.

Project Genomic Variant Insight Engine – LLM-Powered Genomics Interpretation Platform at Accenture

May 1, 2022 - May 31, 2024

Built an end-to-end genomics ML pipeline using RNA-seq preprocessing (alignment, normalization, DESeq2) and XGBoost/Random Forest models to identify high-risk gene signatures. Developed an LLM-powered RAG system (GPT-4/LLaMA + NCBI/ENSEMBL) to generate clinician-friendly variant insights and deployed scalable inference with AWS Lambda + API Gateway, supported by Power BI/Tableau dashboards.

Enterprise AI Compliance Analyst – LLM + RAG System for Document Understanding at Accenture

May 1, 2022 - May 31, 2024

Built an LLM-powered RAG system using GPT-4/LLaMA, LangChain, and Pinecone to automate compliance document search, summarization, and policy comparison via a FastAPI service. Deployed with Docker + AWS EC2, added Whisper for voice queries, and created dashboards to track document insights and compliance gaps.