Looks like you have JavaScript disabled. For the full Twine experience, you will need to re-enable it.

I am an AI/ML Engineer who blends research with hands-on deployment to build production-ready, inference-optimized ML systems. I specialize in LLM-powered agentic solutions, RAG pipelines, and end-to-end MLOps, delivering reliable AI in production using Python, PyTorch, Docker, LangChain, and AWS. My work combines CMU-level research with real-world deployment, enabling measurable impact such as 35% faster check-ins and 10% operational gains in logistics. I enjoy turning complex algorithms into scalable, secure systems and bridging the gap between theory and practice to solve meaningful problems.…I am an AI/ML Engineer who blends research with hands-on deployment to build production-ready, inference-optimized ML systems. I specialize in LLM-powered agentic solutions, RAG pipelines, and end-to-end MLOps, delivering reliable AI in production using Python, PyTorch, Docker, LangChain, and AWS. My work combines CMU-level research with real-world deployment, enabling measurable impact such as 35% faster check-ins and 10% operational gains in logistics. I enjoy turning complex algorithms into scalable, secure systems and bridging the gap between theory and practice to solve meaningful problems.

Kiran Prasad Jamuna Prasad

Data Scientist, AI Engineer, Data Analyst, +3





I am an AI/ML Engineer who blends research with hands-on deployment to build production-ready, inference-optimized ML systems. I specialize in LLM-powered agentic solutions, RAG pipelines, and end-to-end MLOps, delivering reliable AI in production using Python, PyTorch, Docker, LangChain, and AWS. My work combines CMU-level research with real-world deployment, enabling measurable impact such as 35% faster check-ins and 10% operational gains in logistics. I enjoy turning complex algorithms into scalable, secure systems and bridging the gap between theory and practice to solve meaningful problems.…I am an AI/ML Engineer who blends research with hands-on deployment to build production-ready, inference-optimized ML systems. I specialize in LLM-powered agentic solutions, RAG pipelines, and end-to-end MLOps, delivering reliable AI in production using Python, PyTorch, Docker, LangChain, and AWS. My work combines CMU-level research with real-world deployment, enabling measurable impact such as 35% faster check-ins and 10% operational gains in logistics. I enjoy turning complex algorithms into scalable, secure systems and bridging the gap between theory and practice to solve meaningful problems.

Available to hire

I am an AI/ML Engineer who blends research with hands-on deployment to build production-ready, inference-optimized ML systems. I specialize in LLM-powered agentic solutions, RAG pipelines, and end-to-end MLOps, delivering reliable AI in production using Python, PyTorch, Docker, LangChain, and AWS.

My work combines CMU-level research with real-world deployment, enabling measurable impact such as 35% faster check-ins and 10% operational gains in logistics. I enjoy turning complex algorithms into scalable, secure systems and bridging the gap between theory and practice to solve meaningful problems.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Language

English

Fluent

Work Experience

Data Scientist - Application Developer at Carnegie Mellon University

May 1, 2025 - Present

Developed a privacy-centric Flutter application with client-side geofencing and QR/6-digit code validation to automate attendance and certificate eligibility. Reduced check-in times by 35% and cut false-negative rejections by 12% through A/B testing on validation latency and geofence radius calibration using Firebase Remote Config.

Data Scientist at Carnegie Mellon University

January 1, 2025 - May 1, 2025

Automated high-volume ETL pipelines to synchronize check-in/check-out datasets, cutting processing lag by 40%. Recovered 18% of incomplete records via data wrangling and temporal correlation for imputing missing User IDs; transformed unstructured raw logs into structured formats with Pandas.

Graduate Researcher at Carnegie Mellon University (Guided by Prof. Chrysanthos Gounaris)

November 1, 2023 - December 1, 2024

Engineered a Graph Attention Network (GAT) and Deep Reinforcement Learning framework to optimize large-scale logistics, achieving 10% efficiency gains and improving routing efficiency; implemented Encoder-Decoder architecture with multi-head attention for high-dimensional graphs.

Mathematics Tutor at PLUS (Personalized Learning Squared)

September 1, 2024 - January 1, 2025

Delivered personalized mathematics instruction to diverse learners, leveraging interactive digital tools; tailored feedback and mentorship improved learning outcomes and STEM confidence.

Head of Design at IIChE BMSCE Student Chapter

August 1, 2021 - July 1, 2023

Led visual identity and branding; produced marketing assets recognized by university management. Served as Chief Editor for ChemZone-2023, overseeing design and publication using Adobe Creative tools and standardizing design protocols.

Data Scientist, Application Developer at Carnegie Mellon University

May 1, 2025 - February 1, 2026

Architected a secure, privacy-compliant Flutter application with client-side geofencing and QR/6-digit code validation, enforcing secure-by-default practices and full compliance with institutional data governance. Engineered spatial outlier detection in ETL pipelines to flag GPS spoofing and location anomalies, and integrated SAML SSO into a hybrid GCP Firestore and SQL backend for secure, scalable institutional access. Reduced check-in processing time by 35% and false negative rejections by 12% through A/B testing on geofence radius and validation latency, aided by Firebase Remote Config and statistical hypothesis testing.

Data Scientist, IT-Support at Carnegie Mellon University

January 1, 2025 - May 1, 2025

Designed and deployed automated ETL pipelines to synchronize high-volume institutional datasets, reducing processing lag by 40% and eliminating manual bottlenecks. Enforced schema validation and metadata standards for downstream analytics.

Graduate Researcher at CMU - Gounaris Lab (Linde Inc. collaboration)

November 1, 2023 - December 1, 2024

Delivered 10% operational efficiency gain for Linde Inc. by engineering a Graph Attention Network (GAT) and Deep Reinforcement Learning (DRL) framework in PyTorch to optimize large-scale industrial logistics, outperforming Google OR Tools and DQN baselines. Achieved 62% routing efficiency on high-dimensional graphs using an Encoder–Decoder with Multi-Head Attention and Dynamic Node Embeddings; designed training pipelines with reward shaping and curriculum learning for robust DRL convergence.

Head of Design and Chief Editor at IIChE BMSCE Student Chapter

August 1, 2021 - July 1, 2023

Led end-to-end design, editorial, and publication workflow for the ChemZone-2023 annual magazine as Chief Editor, managing a cross-functional creative team and standardizing design protocols across marketing assets using Adobe Photoshop and InDesign, earning official university recognition. Authored a published article on neural networks applications in the chemical engineering industry, bridging AI/ML research with domain-specific engineering contexts.

Machine Learning Intern at Indian Institute of Chemical Engineers

November 1, 2022 - May 1, 2023

Boosted solar panel defect classification validation accuracy by 22% (61% to 83.05%) across six defect categories by replacing an overfitting 11M-parameter CNN with a fine-tuned EfficientNet transfer learning pipeline. Deployed a real-time computer vision web application using Streamlit and TensorFlow/Keras on AWS EC2, delivering top-3 predictive confidences from uploaded images to non-technical users with continuous background hosting via nohup. Accelerated architecture selection for multi-class image classification by benchmarking deep learning models including CNN, MobileNetV2, and EfficientNet, managing the full ML pipeline with Git version control.