Maanaav Motiramani

University of New South Wales

• Weighted Average Mark (WAM): 78.7 | Specialization: Computational
• Recipient of UNSW International Scientia Scholarship (A$40K), awarded for outstanding academic merit & leadership.

Relevant Coursework: Machine Learning & Data Mining, Database Systems, Big Data Management, Data Visualisation, Empirical Methods for Causal Inference, Statistical Inference, Foundations of Computer Science, Data Structures & Algorithms, Multivariate Analysis, Business Economics, Economics of Strategy, Information Systems Consulting

SVKM's NMIMS Mukesh Patel School of Technology Management & Engineering (MPSTME)

• CGPA: 3.91/4 | Honours in Data Science/Analytics
• Awarded Technical Achievement Award (1 of 3 students for research & innovation), Meritorious Student Award (Dean's List) (Ranked Top 12 in graduating batch) and selected as Student Ambassador (1 of 8 students) for contribution to institute representation.

• Predicting Mars dust storm intensity from pre-storm atmospheric conditions by merging storm catalog (MDAD) with dust opacity climatology (Montabone CDOD) across 5 Mars years.

• Building an A-MEM agentic memory pipeline using LLM extraction (GPT-4.1-mini) and MongoDB Atlas Vector Search to deliver personalised tutoring experiences on an EdTech platform with 2,000+ user signups.

• Building a real-time Agentic AI safety system for construction sites; secured US$100K in Microsoft for Startups Azure credits; Columbia AI Startup Challenge Semifinalist; selected for the UNSW Pre-X Accelerator (25/300 selected); interviewed with Y Combinator and other accelerators (<3% reach YC stage).
• Architected a multimodal vision pipeline using LLaVA and YOLOv11 to perform zero-shot hazard reasoning on live site feeds.
• Containerized and deployed the FastAPI backend to Azure Container Apps using Docker and Azure Container Registry (ACR).

• Shipped a full-stack MVP to 100+ pilot users (UNSW Peter Farrell Cup Top 10 Finalist); engineered a two-stage mentor-matching pipeline using LLM multi-label classification and LLM-as-a-judge A/B testing, improving match quality and user trust.
• Architected Next.js (TypeScript) app with Google Analytics events to track user behavior, designed Airtable data model and automation hooks to recompute matches on specific events.
• Contributed to product discovery, user-journey mapping, and UX workshops to turn insights into features.

A project-based company that collaborates with various clients to deliver AI and automation solutions.

• Built a semantic search workflow over a 10K+ MCQ repository using BERT embeddings, reducing manual drafting workload by 25%, resulting in faster content creation cycles for educators.
• Fine-tuned Seq2Seq T5 on educational Q&A pairs to automate Question Generation, reducing test creation time by ~5 min per question, resulting in quicker quiz/test generation at scale.
• Built a churn prediction model (Logistic Regression, Random Forest) on 3K student/parent survey responses (18.7% churn); achieved ROC-AUC 0.86 (5-fold cross-validation), enabling targeted retention outreach.

A lead generation company aggregating vast amounts of financial and corporate data for decision-making.

• Optimized query performance by migrating 20M records from SQL to Elasticsearch leading to faster searches.
• Engineered an NLP pipeline (spaCy NER, regex parsing) to extract 50K+ structured records from unstructured PDFs.
• Architected 12+ Python scrapers (Scrapy, Selenium) and API integrations to extract data from government & public sources, automating updates for 20K+ records daily and saving 8 hours/week, improving data freshness for decision-making.
• Engineered an internal analytics platform using React/FastAPI to audit data quality, eliminating the marketing team's dependency on engineering support for lifecycle management.

• Built a Flutter image management app (Industry-Academia Partnership) with duplicate detection, geo-tagging, and image enhancements, reducing image search time by 70%.