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Resume (PDF, opens in a new tab)

Pratyush Agarwal

I like building things people actually use.
Three years shipping data systems in production, now all in on software engineering.
Proof, not promises, one commit at a time.

Currently building Mnemo

Resume (PDF, opens in a new tab)See what I'm buildinghi@pratyushagarwal.com (opens email)

Now

A dated feed of what I ship — commits, writes, and milestones — click each item to know more.

184 contributions in the last year

LessMore

  1. Built the same way as everything it lists: a design doc, a dependency-ordered plan, one commit per verified task. The site is not a writeup about the pivot, it is the pivot, running in public.

    View Repo (opens in a new tab)

  2. The real lesson was not any single bug. An AI coding agent will happily produce code that runs, looks fine, and is quietly wrong. Planning first and checking both behavior and structure is what caught a login timing leak, a broken migration, and a wrong uniqueness rule.

    View Post (opens in a new tab)

  3. The CRUD core is done and clean-slate verified, the floor the rest stands on. Auth comes next, then a semantic graph, then an agent layer on top. Nine milestones, each with its own design doc and learning write-up.

    View Repo (opens in a new tab)

  4. Public from commit one changes how you build. Every choice gets a written reason before the code, because the reasoning is part of the repo. The discipline is the point, not a side effect of it.

    View Repo (opens in a new tab)

  5. The login endpoint only hashed the password when the email existed, so an unknown email answered faster than a wrong one, quietly leaking which accounts are real. The fix: one bcrypt check every attempt, real hash or dummy, so both paths cost the same and return an identical 401.

    View Repo (opens in a new tab)

fraud reduction at Apna within one month
~20%
BigQuery warehouse spend cut at Apna
~$2,500/mo
manual reporting saved at Kohler
~10 hrs/wk
Claude Builder Club Hackathon (UIUC)
2nd place
published (Springer, IEEE, Elsevier)
3 papers

Projects

Selected builds — filter by tag, click to expand.

  • Problem

    Notes, tasks, and links live in disconnected tools that never show how anything interlinks, and none of them actually know you.

    Approach

    Two services over an HTTP contract: a NestJS API for notes, tasks, auth, and kanban, and a FastAPI AI layer, orchestrated with Docker Compose. Strict layering: thin routers, service logic, repository DB access. Nine stacked milestones from plain CRUD to pgvector semantic search, GraphRAG over Neo4j, and an MCP-exposed agent.

    Outcome

    In active development, documented milestone by milestone with design docs, plans, and learning write-ups in the repo.

  • Problem

    Public-company research is fragmented across EDGAR, market data sites, and filing types, and generic AI chat answers without citing filings.

    Approach

    One workspace per ticker: filings, fundamentals, charts, governance, and TickerChat, a Claude-powered assistant grounded in official SEC documents, with PDF export. Next.js frontend on Vercel, FastAPI ingestion service on Render.

    Outcome

    Placed 2nd at the hackathon. Live demo online.

  • Problem

    Raw city-scale transit data is useless without curation, and dashboards alone don't answer ad-hoc questions.

    Approach

    PySpark pipeline over AWS S3 producing curated datasets; XGBoost, Random Forest, and LSTM models for fare and demand forecasting; models served via a FastAPI MCP server and a conversational agent built with Strands Agents on AWS Bedrock.

    Outcome

    End-to-end: raw data to models to a chat interface for real-time mobility insights.

  • Problem

    Repetitive multi-service workflows burn hours and break silently when APIs rate-limit or duplicate work.

    Approach

    Node.js pipeline using the Claude API for evaluation and generation, Google Sheets and Drive for state, Apollo for enrichment. Built around production concerns: per-task model routing, safe re-runs that skip completed work, rate limiting, deduplication, OAuth per provider, and a human approval gate at every stage.

    Outcome

    Powers a real job search end to end, but the design generalizes to any service-orchestration workflow.

Publications

Peer-reviewed papers — click to expand for the abstract.

  • Read paper (opens in a new tab)

    This research introduces a task scheduling algorithm inspired by Artificial Neural Networks (ANN), designed to deliver superior power efficiency and scalability compared to existing methodologies. By leveraging the predictive capabilities of ANN, the algorithm achieves constant-time predictions, even as task loads scale significantly.

    Experimental evaluations using real log files demonstrate performance across metrics such as average start time, finish time, execution time, resource utilization, task failure rates, and power consumption. The model's robustness is proven in both underloaded and overloaded data center conditions, ensuring efficient task scheduling while maintaining system stability under dynamic workloads.

  • Read paper (opens in a new tab)

    This review examines the critical challenges facing fog and edge computing architectures, which play a pivotal role in managing the explosion of sensor-driven data. The study highlights pressing issues such as security vulnerabilities, network latency, and inefficient resource utilization, providing an in-depth analysis of existing solutions.

    The paper explores advanced approaches, including nature-inspired algorithms and metaheuristic optimization techniques, as promising solutions to enhance the performance of fog and cloud computing systems, serving as a guide for researchers and practitioners in next-generation computing environments.

  • Read paper (opens in a new tab)

    This paper investigates the application of the Harmony Search Algorithm (HS) for task scheduling in cloud environments, offering a comparative analysis against Genetic Algorithms (GA) and Moth Search Algorithms (MSA). Simulations conducted using CloudSim and MetaheuristicOpt packages reveal HS as a highly efficient and cost-effective solution for dynamic task scheduling.

    The study evaluates performance across key metrics, demonstrating HS's superiority in optimizing execution time, resource allocation, and operational costs, setting a foundation for further exploration in metaheuristic-based task scheduling methodologies.

Experience

Roles and internships — click to expand for detail.

  • Key responsibilities

    • Built an ETL pipeline with Azure Data Factory and Databricks to ingest SharePoint and on-prem SQL Server data into Parquet-based raw and curated layers, enabling clean, analytics-ready datasets for downstream use.
    • Integrated on-prem operational databases with Azure cloud platforms, building the data flow that supports company-wide analytics and upcoming AI automation initiatives.
    • Designed data models from on-prem SQL Server schemas and built Power BI reports on top of them, creating ER diagrams and a data dictionary to keep reporting consistent and scalable across teams.
    • Built and maintained 6 Power BI dashboards tracking manufacturing KPIs, improving plant leadership visibility and saving around 10 hours of manual reporting each week.
    • Analyzed Ignition form submissions across plants, spotting schema inconsistencies and recommending structural fixes to protect long-term data model integrity.
    • Introduced Git-based version control for pipelines and reports to the team, improving governance across the org.

  • Key responsibilities

    • Led cross-functional coordination for a quantum satellite-themed game, aligning sponsors, programmers, design, and art to ship the project in 2 months and around $2,000 under budget.
    • Prioritized the development backlog by weighing core gameplay value, implementation complexity, and system dependencies, so critical features shipped first and bottlenecks cleared fast.
    • Ran an Agile workflow tracking milestones, tasks, bugs, and dependencies, keeping the team on schedule through resource constraints and shifting timelines.
    • Managed maintenance and optimization of a game with 20,000+ downloads, resolving bugs, improving stability, and supporting adaptation for a broader global audience.
    • Worked closely with senior production leadership to validate planning decisions, refine execution, and keep stakeholders aligned throughout development.

  • Key responsibilities

    • Built a user data extraction and ingestion pipeline using SQL, Python, Postman, and Government of India APIs, cutting processing time for Operations agents by ~50% and improving onboarding for ~1,200 users weekly.
    • Integrated third-party government identity APIs and designed user verification workflows that reduced sign-up drop-offs by ~10% and improved onboarding conversion.
    • Optimized high-cost BigQuery workloads through partition-filtering and query scope-limiting, cutting warehouse spend by ~$2,500 monthly and improving execution time by ~20%.
    • Consolidated fragmented operational data sources into a single unified tracking system, reducing investigation overlap by ~35% and saving 20+ agent hours weekly.
    • Built a SQL-based fraud detection workflow in Metabase and Retool that cut fraudulent recruiter activity ~20% within a month, strengthening platform security.
    • Built Metabase and Mixpanel dashboards to track operational metrics and support fraud detection investigations across Operations and Trust and Safety.
    • Partnered with Operations, Trust and Safety, Product, and Business stakeholders to improve workflows and drive measurable process gains.

  • Key responsibilities

    • Built web scraping scripts using ChromeDriver and Selenium, plus the database and supporting infrastructure to capture and analyze daily website metrics for brand and sales insights.
    • Collaborated with developers to build scalable scripts and infrastructure supporting market intelligence data gathering and recurring analytics workflows.
    • Integrated Monday.com with Slack to automate team operations, improving efficiency by ~25%.
    • Built 13 Looker Studio dashboards and biweekly reports synthesizing market trends and competitor insights for leadership decisions during the Pixel 6 launch.
    • Analyzed market and sales data to identify key growth drivers and channel opportunities, helping improve brand presence and inform go-to-market strategy.
    • Led and coordinated a team of 3 interns, setting up processes that improved collaboration, execution, and delivery quality; role extended from 1 to 7 months.

Writing

Notes on building in public — Substack and LinkedIn.

Follow along on Substack (opens in a new tab)

About

Pratyush Agarwal

I spent three years working with data across Kohler, Apna, and Google, building pipelines, automation, and systems that teams relied on every day. Somewhere along the way I realized the parts I loved most were the software parts: designing APIs, shipping products, watching people use what I built. So I'm going all in on software engineering.

I don't have years of a traditional SWE / SDE / Product Engineer title behind me, but I have production experience, I learn fast, and I'm building the proof in the open: every project public, every milestone logged, every lesson written up. If you want to see how I work, it's all here.

Education

  • University of Illinois Urbana-Champaign

    2024–2026

    M.S. in Information Management

  • Manipal University Jaipur

    2018–2022

    B.Tech in Computer and Communication Engineering