Ruchir
Jadhav.
SWE @ Easley Dunn · MS CS @ USC
Building resilient cloud systems and distributed backends. Passionate about AI/ML, systems engineering, and end-to-end software.
I am a Computer Science graduate student at the University of Southern California with experience across cloud infrastructure, backend and mobile systems, data engineering, and machine learning.
I currently work as a Software Development Engineer at Easley Dunn Productions, building the Node.js/Express backend for SafetyNet, a cross-platform React Native app, covering authentication, real-time messaging, and geospatial features.
Previously, I was a Software Development Engineer Intern at Amazon Web Services, where I built resilient backend systems for the Amazon EKS Metrics Service, and a Data Engineer at BusinessNext, building ETL pipelines and query-driven dashboards for a banking CRM.
My projects span real-time multimodal fact-checking, multimodal emotion detection, cooperative autonomous-vehicle intersection management, and database internals — including adding materialized views to DuckDB.
I am looking for graduate software engineering roles where I can apply strong systems, algorithms, backend, and full-stack engineering skills.
Contact
Looking for
Graduate SWE roles — backend, cloud, distributed systems, AI/ML.
Feb 2026 – Present
Software Development Engineer
Easley Dunn Productions
- ›Built the Node.js/Express backend for SafetyNet, a cross-platform React Native app, implementing JWT + Firebase OTP authentication, a CTE-based social-graph discovery algorithm with Haversine proximity ranking, and three-tier rate limiting over PostgreSQL.
- ›Engineered real-time messaging via Socket.IO with JWT-authenticated WebSocket handshakes, AES-256-GCM encrypted storage, and atomic PostgreSQL transactions for message persistence and notification fanout.
- ›Shipped 46+ merged pull requests across backend and mobile, including a three-tier-fallback university autocomplete proxy, a Leaflet map WebView with geospatial filtering, and 40+ Firebase Analytics events across major user flows.
May 2025 – Aug 2025
Software Development Engineer Intern
Amazon Web Services (AWS)
- ›Built a four-component backfill system in Go for the Amazon EKS Metrics Service, resolving CloudWatch API outages that had caused up to 6-hour metric data gaps, with failure detection, hierarchical data preservation, and chronological replay for zero data loss.
- ›Designed a hot-reloadable failure simulation framework with per-log-group injection rates (0–100%) and S3-synced JSON config for safe outage testing in beta, validated end-to-end via before/after CloudWatch dashboard comparisons.
- ›Implemented offset-timed reprocess (30s) and republish (45s) services with AWS-specific retry/backoff, CloudWatch Logs batching, and S3 lifecycle tiering (Standard→IA→Glacier), shortening outage recovery time and ensuring continuous data availability.
Sep 2023 – May 2024
Data Engineer
BusinessNext
- ›Built and refined SQL queries on Linux using Django ORM to power client-portfolio dashboards for Relationship Advisors in BusinessNext's banking CRM, enabling the frontend team to deliver responsive reporting interfaces.
- ›Developed data logic to surface product recommendation opportunities for banking clients, translating Relationship Advisor requirements into structured queries that identified better-fit financial offers from existing account data.
FACTUAL — Real-Time Multimodal Fact-Checking
- ›Built a real-time speech fact-checking pipeline: Whisper ASR transcription, LLM-based claim extraction, and retrieval-augmented verification against live web evidence.
- ›Constructed a 400-claim benchmark split across pre- and post-model-cutoff claims and converted to speech-like audio to stress-test the pipeline under realistic conditions.
- ›Added a self-critique loop where a judge LLM evaluates evidence sufficiency and iteratively refines search queries, up to 10 iterations, to reduce hallucination.
- ›Best configuration (GPT-4o, agentic) reached 84% accuracy and 0.83 F1 on post-cutoff claims, versus 63% accuracy for zero-shot prompting alone.
- ›Prototyped the retrieval layer as a standalone tool (SearchAPI) using the Gemini API and Google Custom Search for article retrieval, summarization, and batch claim processing with structured JSON output.
Multimodal Emotion Detection
- ›Balanced the MELD dataset (2,000 train / 400 dev samples per class) via audio pitch/speed/noise augmentation and OpenCV-based video filters to correct severe class imbalance.
- ›Fused three modalities — BERT (text, with dialogue context), HuBERT (audio embeddings), and OpenFace facial Action Units (video) — using weighted late fusion (50% text / 30% audio / 20% video).
- ›Fine-tuned a LoRA-augmented LLM on facial Action Unit features to generate natural-language emotion explanations grounded in AU activations.
- ›Weighted late fusion reached 60% accuracy and 38% macro F1, roughly double the 23% accuracy of early fusion; shipped a Streamlit app for end-to-end video-to-emotion inference.
Materialized Views in DuckDB
- ›Extended DuckDB's parser, logical planner, and execution engine to support CREATE/REFRESH MATERIALIZED VIEW, with dependency tracking to auto-invalidate views when base tables change.
- ›Stored materialized views in DuckDB's native columnar format so they inherit its indexing and compression, and modified the query planner to prefer them automatically when applicable.
- ›Benchmarked against standard views on TPC-H queries: retrieval time dropped from ~0.3s to ~0.004s (up to 100x faster) at the cost of ~25% additional storage and higher one-time creation overhead.
- ›Validated scalability up to 2TB datasets and confirmed safe concurrent refresh/query access via locking and transaction control.
CARLA Cooperative V2X Intersection
- ›Designed a V2X-inspired virtual Intersection Manager that replaces traffic lights with an FCFS, conflict-aware reservation protocol for autonomous vehicles in CARLA.
- ›Built a lightweight mailbox communication layer for REQUEST/PERMIT/CROSSING/CLEARED message exchange between the manager and per-vehicle agents, with geometry-based conflict-box detection for entry/exit.
- ›Benchmarked against fixed-cycle traffic signals: 51% lower average crossing time (2.56s to 1.25s/vehicle), 105% higher throughput (48 vs. 23.4 cars/min), and 51% faster total queue clearance.
- ›Logged arrival, permission, entry, and exit timestamps to CSV for reproducible performance analysis across synchronous simulation runs.
Travel Recommendation & Route Optimizer
- ›Scraped TripAdvisor via Selenium for the top ~120 rated spots across 5 Indian cities, geocoding each with GeoPy into a ratings/category dataset.
- ›Fetched the user's live location and used the Google Maps API to compute real driving distance and duration to every catalogued spot.
- ›Applied KNN to shortlist the 7 closest matching spots, then benchmarked Tabu Search, Genetic Algorithm, and Simulated Annealing to sequence an efficient visiting route.
- ›Simulated Annealing found the shortest routes in testing, but Tabu Search converged 5-8x faster, making it the practical pick under time constraints; visualized routes with Folium/OpenStreetMap.
Sign Language Detection
- ›Built a real-time gesture recognition system for accessibility-oriented use cases.
- ›Collected and processed a diverse hand-sign dataset using feature extraction, normalization, and augmentation.
- ›Improved model quality through hyperparameter tuning, transfer learning, dropout, and regularization.
- ›Achieved 93%+ accuracy while maintaining low-latency inference.
Languages
Frameworks & Libraries
Databases
Tools, Cloud & DevOps
University of Southern California
Master of Science in Computer Science
Los Angeles, CA
Coursework
University of Mumbai
Bachelor of Technology in Computer Engineering
Mumbai, India
Let's build
something
together.
I'm actively looking for graduate SWE roles in cloud, backend, distributed systems, and AI/ML. If you have an opportunity or just want to connect — reach out.
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