Daksha Ladia

Featured case studies

• Healthcare Document Processing Automation

  Sold + deployed an AI document pipeline for insurance workflows; reduced manual processing by 99% and supported 5000+ documents/day.

  GitHub

• KYC Identity Verification System

  Built an end-to-end document understanding service (classification → extraction → validation → audit logging) with ~95% field accuracy and ~3s latency per document.

  GitHub

• Multi-Agent Browser Automation Platform

  Designed a vision-LLM automation MVP with structured reasoning loops, state detection, and dataset generation for reliability under changing UIs.

  GitHub Watch Demo

Context engineering

I focus on getting LLM systems to work reliably under real constraints: ambiguous questions, incomplete schemas, noisy documents, changing UIs, and adversarial inputs. I design context so the model can make correct decisions consistently, not just once.

• Retrieval quality: chunking strategies, query synthesis/expansion, hybrid retrieval, reranking, and grounded answers
• Reliability: eval sets, regression testing across prompts/models, and automated comparisons
• Tool use: structured outputs, guardrails, and feedback loops (e.g., execution-based validation for NL→SQL)
• Security: prompt injection awareness, data leakage prevention, and robust failure modes

Work Experience

• Machine Learning Engineer Intern | System1 | May 2025 - Aug 2025

  • Built a production-grade LLM system for natural language to SQL over business data (RAG + retrieval + feedback loops), achieving 98% execution success on real queries
  • Designed evaluation pipelines to measure correctness and regression across the model (AWS Bedrock) and prompt changes
  • Containerized the system with Docker, enabling reproducible experiments and rapid iteration

• Personal Project: Automatation of Healthcare Document Processing Platform| Freelance | June 2024 - Aug 2024

  Built and sold enterprise healthcare document processing platform to medical insurance company. Architected end-to-end AI pipeline using GPT-4o and Vision API for document classification and OCR, reducing processing time by 99% and enabling thousands of daily document processing with high accuracy.

• Software Engineer | Microsoft, Bing Ads | June 2020 - July 2024

  • Optimized a large-scale ad simulation system to replay online auction traffic offline, enabling evaluation of ranking changes and ad performance across millions of users and 2TB daily production data pipelines
  • Reduced compute cost by 9% and improved simulation efficiency by 15% through stratified sampling strategies, function-level optimizations, and evaluation restructuring for high-throughput data
  • Designed and deployed a regression testing framework that automated parallel evaluation across feature-branches, replacing manual workflows; reduced manual effort by ~90% through standardized evaluation pipelines and automated comparisons
  • Led A/B experimentation and rollout of ranking features tuning on production traffic, improving advertiser conversions by 4% through data-driven optimization and iterative model improvements

• Data Science Intern | FarmGuide (Acquired by DeHaat) | Jan 2020 - May 2020

  Developed ML models for crop identification and health monitoring with 96% accuracy. Built forecasting algorithms using ARIMA and Prophet for environmental insights and crop recommendations.

• Software Engineering Intern | Microsoft, Bing Ads | May 2019 - July 2019

  Analyzed seasonality patterns in advertiser campaigns using time-series modeling. Optimized bid pricing algorithms for peak holiday periods.

Selected Projects

• Semantic Pre-training Strategies for Language Models | Allen Institute Research Extern

  • Problem: improve downstream generalization via better pre-training signals
  • Approach: semantic-driven pre-training and structured knowledge integration
  • Stack: Python • contrastive learning • representation learning
  • Outcome: research strategies + experiments toward stronger language understanding

• KYC Identity Verification System

  • Problem: manual KYC verification was slow and non-scalable
  • Approach: end-to-end pipeline (classification → vision extraction → validation → audit logging)
  • Stack: Python • OCR • Fireworks AI • AWS
  • Result: >95% field accuracy with a complete audit trail for compliance

• Multi-Agent Browser Automation Platform

  • Problem: web automation breaks under UI drift and hidden state
  • Approach: vision + DOM signals, structured reasoning loops, and state-aware decision making
  • Stack: Python • Gemini/vision LLMs • browser automation
  • Result: improved task completion reliability in a controlled environment + automated dataset generation
  Watch Demo

• Recomm AI - Privacy-Preserving Recommendation System

  • Problem: build personalization without leaking user data
  • Approach: differential privacy (gradient clipping + noise) + federated learning
  • Stack: Python • privacy ML • evaluation
  • Result: validated robustness using membership inference + adversarial testing

• On-Device Privacy-First AI Companion

  • Problem: deliver a helpful companion without sending sensitive data to servers
  • Approach: on-device multimodal assistant + federated personalization
  • Stack: on-device inference • voice + text
  • Result: personalized adaptation to tone/mood without data leaving the device
  Watch Demo

• Healthcare Document Processing Automation

  • Problem: manual claim document processing was slow and costly
  • Approach: OCR + classification + unstructured-to-structured extraction with validation
  • Stack: GPT-4o • Google Vision API • Python
  • Result: 99% time reduction in document processing

• Enhanced Information Retrieval via Query Synthesis

  • Problem: ambiguous queries reduce retrieval quality
  • Approach: segmentation + pseudo-query generation + embedding search
  • Stack: FAISS • vector search • NLP
  • Result: 4% precision improvement over BM25 baseline

• Question Answering Bot

  • Problem: users needed fast, grounded answers from internal documents
  • Approach: ingestion + retrieval + context-grounded generation
  • Stack: LangChain • OpenAI API • PDFs/JSON
  • Result: automated QA pair generation + context-aware responses
  Watch Demo

• LLM Bias Analysis Research

  • Problem: quantify and compare occupational/pronoun bias across LLMs
  • Approach: statistical testing + controlled prompts + comparative analysis
  • Stack: evaluation • statistics
  • Result: published analysis + mitigation strategies (Submitted to COLM'25)

• Real-Time GitHub Trending Prediction

  • Problem: detect emerging repositories early from high-volume event streams
  • Approach: streaming ingestion + feature generation + neural prediction
  • Stack: Apache Kafka • Python
  • Result: pipeline processing thousands of events/sec with trend forecasts

• AI SlackBot

  • Problem: teams lose context in long-running Slack threads
  • Approach: summarization + retrieval over channel history + Q&A
  • Stack: LLMs • Slack automation
  • Result: faster onboarding + higher team throughput via searchable context
  Watch Demo

Publications

• Virtual Community: An Open World for Humans, Robots, and Society

  Multi-agent embodied AI simulation platform built with Unity and Python. Generated benchmarks for speed and resolution across robots, agents, and scenes. Accepted at ICLR 2026

• Do Different Models Differ in their Bias Towards Occupational Preferences?

  Comprehensive analysis of pronoun and occupational biases in LLMs using statistical tests. GPT-4o: <5% non-preferred pronoun selection; Qwen2.5: 100% positional bias. Submitted to COLM'25

Research Interests

Context Engineering • Post-Training & Alignment • LLM Inference • Multi-Modal LLMs • Multi-Agent Systems • Bias Detection & Mitigation • Privacy-Preserving ML

Recognition

• Innovation & Excellence Award

  Microsoft (FY20-21) — Pipeline migration project improving operational efficiency

• Judge & Mentor

  MIT Hacks 2025 (Judge) • HackHarvard 2024 (Mentor) • She Hacks DTU 2021 (Mentor)

• Reviewer

  Grace Hopper Celebration 2025 — Application review across multiple verticals

• MIT Policy Hackathon 2024

  Proposed interpretable solution for SpO2 measurement error analysis

• Top 1% National Exams

  Inspire Scholarship from Government of India (2015)