|
Records/sec Event Streaming |
Bytecode Opcodes Custom Compiler |
Tests Across All Projects |
LRU Eviction Cache Server |
Agent Orchestration Platform |
Students Mentored in C++ Systems |
🧱 I don't use frameworks to learn — I build from scratch.
Compiler → lexer, Pratt parser, type inference, 28-opcode bytecode compiler, stack VM with mark-sweep GC (C++20)
Streaming Engine → commit log, TCP broker, wire protocol, producer/consumer SDKs, LZ4 compression (Rust)
Transformer → GPT architecture from scratch — RoPE, RMSNorm, BPE tokenizer, MQA ablation (PyTorch)
Cache Server → O(1) LRU eviction, TTL, sharded thread-safe storage, benchmarked latency (C++20)
Kafka-inspired event streaming engine built from scratch in Rust. Commit log, TCP broker, custom wire protocol, producer/consumer SDKs, LZ4 compression, consumer groups. 304K records/sec throughput. |
Programming language built from scratch in C++20. Lexer, Pratt parser, type inference, 28-opcode bytecode compiler, stack-based VM with mark-sweep garbage collector. 6 example programs. |
Enterprise AI agent orchestration with 6 modules (core/connect/train/eval/observe/api). LoRA fine-tuning for Qwen-2.5, structured evaluation pipelines. 207 tests, 34 commits. |
Real-time ML monitoring with PSI drift detection, Prometheus metrics, WebSocket live updates. FastAPI + React. Live Demo → |
Redis-inspired TCP cache server with O(1) LRU eviction, TTL expiration, sharded thread-safe storage. Benchmarked p50/p95/p99 latency. Cross-platform CI with sanitizer builds. |
GPT transformer built from scratch in PyTorch. RoPE positional encoding, RMSNorm, BPE tokenizer, multi-query attention ablation study. 112 tests. |
Autonomous research agent with DAG-based hierarchical planner, ReAct tool-use loop, evidence critic scoring, provider-agnostic LLM failover. 59 tests. |
REST API with JWT auth, RBAC, PostgreSQL data modeling & migrations, Redis caching & rate limiting, health/metrics endpoints. Containerized with Docker Compose + automated CI. |
End-to-end pipeline processing 1,000+ financial docs with intelligent classification, OCR extraction, and embedding-based semantic retrieval. 60% throughput increase. |
Full ML pipeline: EDA → feature engineering → model training. Accurate temperature trend predictions for NYC metro. |
From retail floor to building compilers, streaming engines, and AI platforms.
📅 Full Timeline
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Jan 2026 – Now 🎓 Teaching Assistant — C++ OOP @ Stony Brook
Aug 2025 – Oct 2025 🍎 Sales Specialist @ Apple
May 2025 – Jul 2025 🤖 AI Engineering & Automation Extern @ Outamation
Jul 2024 – May 2025 📱 Retail Mobile Expert @ T-Mobile
Sep 2024 – Nov 2024 🔐 Web3 Security Data Analytics Extern @ Webacy
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🎓 Teaching Assistant — C++ OOP | Stony Brook University Jan 2026 – Present
Mentoring 30+ students in systems-level C++ — covering OOP design patterns, STL containers, dynamic memory management, RAII, and pointer safety. Debugging segfaults, memory leaks, and logic errors. Conducting code reviews emphasizing correctness, modularity, and performance.
🍎 Sales Specialist | Apple Aug 2025 – Oct 2025
Delivered the Apple retail experience — product consultation, hands-on demos, and technical guidance across the full Apple ecosystem. Consistent top performer in customer satisfaction and sales metrics.
🤖 AI Engineering & Automation Extern | Outamation May 2025 – Jul 2025
- Designed and deployed a modular AI document processing pipeline (Python, PyMuPDF, Tesseract OCR, NLP, LLM classification) across 1,000+ financial documents — cutting manual processing time by 60%
- Built a RAG-powered retrieval system with LlamaIndex, contextual chunking, and embedding-based semantic search
- Benchmarked transformer models across latency, context window, and precision trade-offs for production deployment
📱 Retail Mobile Expert | T-Mobile Jul 2024 – May 2025
Consultative sales across T-Mobile's full product ecosystem — devices, plans, home internet, and accessories. Diagnosed technical issues, performed device troubleshooting, and delivered personalized solutions in a high-volume retail environment.
🔐 Web3 Security Data Analytics Extern | Webacy Sep 2024 – Nov 2024
Applied unsupervised ML & clustering to detect anomalous blockchain transactions and smart contract vulnerabilities, achieving 95%+ accuracy in labeled dataset reliability for risk categorization.
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║ ║
║ 🚀 Open to Software Engineering & ML Engineering roles ║
║ 📍 Based in New York — open to relocation & remote ║
║ 🔧 C++ · Rust · Python · Systems · ML · Databases ║
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⚡ @ShamsRupak — shipping code, shipping systems, shipping results.



