Yoga Pose Classification
Machine Learning project done as part of final year of Undergrad.
Bavirisetty Sairam
Learning the Machine while the Machine is Learning.
Bengaluru, Karnataka, IndiaArtificial Intelligence and Machine Learning
3roles
46skills
7education
1credentials
About
Final-year MCA student at Christ University, Bangalore with a BSc Mathematics foundation from Sri Sathya Sai Institute of Higher Learning. Currently working on AI research in fraud detection and deepfake identification - solving real problems that matter in financial services and healthcare. My approach combines mathematical thinking with practical Python implementation to build systems that actually work in production environments. Leadership experience managing institutional events and coordinating teams, plus hands-on work with databases in healthcare settings. This combination gives me unique insight into both fintech innovation and healthcare data challenges. Currently seeking data science roles in Bangalore's fintech or healthcare sectors where I can apply analytical problem-solving to build solutions that create genuine business impact.
Experience
Multimedia Intern
Sri Sathya Sai Premaarpitham Foundation
Sep 2023 - Jun 2024 · Kanakapura, Karnataka, India
• Conducted live audio mixing and photography for events, enhancing the foundation's online presence. • Assisted in revamping the foundation's website, improving user experience and engagement. • Explored a new field outside of tech and CS, gaining valuable multimedia skills and creativity.
Internship Trainee
Sri Sathya Sai Institute of Higher Medical Sciences, Whitefield
Jun 2023 - Jul 2023 · Bengaluru, Karnataka, India
Engaged in learning the basics of HMIS and Oracle database systems at Sri Sathya Sai Institute of Higher Medical Sciences, Whitefield, which provided a solid foundation for my understanding of healthcare technology. • Participated in an introduction session to the healthcare industry, gaining insights into a new field and expanding my knowledge base. • Found the experience interesting and engaging, although further exploration was limited post-internship.
LLM Post-Training Intern
Ethara AI
February 2026 – Present · Remote
Performed structured evaluation of LLM outputs, selected optimal responses, and provided reasoning to guide model quality and alignment. Contributed human feedback signals for response ranking, benchmarking, and dataset refinement.
Education
Christ University, Bangalore
Master of Computer Applications - MCA
Jun 2024
Sri Sathya Sai Institute of Higher Learning
Bachelor of Science - BS, Mathematics and Computer Science
2020 - 2023
Sri Bhagawan Mahaveer Jain College
PCME
Jul 2018 - Mar 2020
Anand Shiksha Kendra
Class V - X
Jun 2012 - Mar 2018
Kendriya Vidyalaya
Class II - IV
Apr 2009 - Mar 2012
CHRIST (Deemed to be University)
Master of Computer Applications
June 2024 – April 2026
Score: 63.77%
Sri Sathya Sai Institute of Higher Learning (Deemed to be University)
BSc (Hons.), Math, Stat & Computer Science
September 2020 – August 2023
CGPA: 6.5/10
Skills
DjangoGitData ScienceMachine Learning AlgorithmsComputer SciencePandas (Software)AlgorithmsArtificial Intelligence (AI)Data StructuresAudio MixingPhotographyEvent PhotographyVideo EditingWebsite BuildingOracle DatabaseMachine LearningSelf LearningProject ManagementIntrapreneurshipMicrosoft PowerPointPythonRSQLAgentic AIPyTorchScikit-learnXAILangGraphCNNTransformersETL ProcessesData PipelinesReal-time ProcessingData ValidationDockerRedisAWSStreamlitNginxFastAPISHAPOpenCVNumPyUNetNLPGroq AI API
Projects
Fraud Detection System
Architected a production-grade multi-agent fraud detection system achieving ROC-AUC 0.8991 on 590K+ transactions using a LangGraph-orchestrated pipeline with SHAP-LLM explainability. Deployed via FastAPI at 880ms latency, optimized to 200–300ms at scale.
DeepHub - AI DeepFake Detection
Built an ultra-lightweight multimodal deepfake detector achieving 81.1% AUC with 330K parameters and a 3.9MB model size. Implemented a three-stream PyTorch architecture processing RGB faces, frequency maps, and facial landmarks across 60K samples.
Identification of Stroke Region
Engineered a preprocessing pipeline using PyTorch, OpenCV, and NumPy for volumetric MRI stroke region detection, achieving 77.59% segmentation accuracy and 65.71% IoU.
Basic Code Analyzer
Developed a production-ready Streamlit web application integrating the Groq AI API for real-time NLP processing, interactive dashboards, automated code quality assessment, structured data export, and 99% uptime.
Volunteering
Volunteer
Sri Sathya Sai Seva Organisation
Apr 2018
Volunteer
Sri Sathya Sai Premaarpitham Foundation
Apr 2020