Hi, I'm Aritro Roy

  • 22-year-old B.Tech Computer Science graduate from Asansol, India.
  • Software Developer with hands-on experience in backend engineering, data engineering, and AI/ML.
  • I am active on X. Follow me on X!
  • Actively learning Rust, AI systems and System Design
  • Outside of coding, I enjoy gaming, performing music live, and weightlifting.
  • I'm passionate about building architectures that can scale easily, automating workflows, and drinking a lot of caffeine.
  • Always curious, always learning.

GitHub Activity

953 contributions in the last year

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Skills

Languages

PythonSQLJavaScriptRust

Frameworks & Tools

FastAPIDjangoAxumTokioSvelteDRFCeleryPyTorchSentence TransformersAirbyte CDKNangoLangfusePytest

Data Engineering & AI

RAG PipelinesElevenLabs STTVertex AIFAISSCross-Encoder RerankingHybrid Search (BM25 + RRF)EmbeddingsETLSchema Design

Databases

PostgreSQLSupabaseMongoDBMySQLRedisQdrantChroma DBPinecone DB

Cloud & DevOps

AWSDockerGitHub ActionsCaddy

Experience

  • Software Development Engineer

    Whatbytes Technologies | Remote

    • Architected a multi-tenant document intelligence platform for commercial real-estate leases on FastAPI — webhook-driven sync across Dropbox, Box, Google Drive and SharePoint (Nango, GCS) feeding a Vertex AI OCR and extraction pipeline that structures 100+ key points per lease into isolated Supabase schemas.
    • Cut end-to-end ingestion latency 60% by parallelizing Celery async stages and optimising schema throughput.
    • Designed Airbyte CDK ETL connectors (full and incremental syncs, 20+ SaaS entities) and a Django REST backend for an AI red-teaming platform, with zero-code YAML tool registration and dependency-graph onboarding.
    • Uncovered and independently verified the production fix for an OAuth defect blocking Google Drive integration in a Rust (Axum, Tokio, SQLite) governance daemon with a Svelte dashboard.
    • Engineered a Stripe-backed billing engine in Django REST on manual-capture PaymentIntents, atomic rollback and row-level locking (select_for_update), eliminating double-spend and duplicate-redemption races across every purchase flow.
    FastAPIVertex AICeleryAirbyte CDKRustStripe

    October, 2025 -Present

  • Backend Engineering Intern

    UrbanRider Technologies | Telangana, Hyderabad (Remote)

    • Resolved PostgreSQL query bottlenecks and REST API inefficiencies to cut application load latency 22%, restructuring indexing to hold performance under peak traffic.
    • Removed manual release steps by automating validation, regression testing and CI/CD with Python, Docker and GitHub Actions, ending environment-parity bugs between staging and production.
    PostgreSQLDockerGitHub Actions

    July, 2022 -May, 2023

Side Projects

  • 1.

    Voice-Enabled RAG Pipeline with Measured Guardrails

    A live voice-to-answer RAG service over a 99K-passage corpus (ElevenLabs STT -> FAISS -> cross-encoder rerank -> Claude) on AWS EC2, holding a P50 of 96ms against a 200ms budget. Cross-encoder reranking lifted recall@5 from 0.848 to 0.916, with per-sentence groundedness guards catching 100% of ungrounded answers at zero false refusals.

  • 3.

    AgentTuring

    A FastAPI query router dispatching requests across Qdrant vector and structured data stores, with tool-use agentic workflows and async concurrency for multi-user workloads, improving semantic query accuracy 25% over a keyword baseline.

  • 4.

    Prospect Pilot - Cold Email Automation

    An AI-powered cold email platform for automated outreach campaigns, with contact extraction, personalized templating, scheduling, and a response-tracking dashboard.

  • 5.

    OptiResume

    a cloud-native resume analysis tool with real-time PDF parsing and LLM integration.

  • 6.

    CourtScraper

    A Python scraper for extracting structured case data from public court records, built for reliable, repeatable data collection at scale.

Impact

60%

faster document ingestion after parallelizing Celery stages

96ms

P50 latency for the RAG pipeline, against a 200ms budget

25%

higher semantic query accuracy over a keyword baseline

0.848 -> 0.916

recall@5 after adding cross-encoder reranking

22%

cut in application load latency from index restructuring