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rehan243/README.md

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GitHub followers  LinkedIn  Kaggle  profile visitors

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about me

i'm an AI/ML engineer based in the US. right now i'm building production AI systems at Reallytics.ai and Verticiti, mostly getting large language models to do useful things in the real world. not demos, actual systems with real users and real traffic.

before this i was at Afiniti and Cloud Kinetics for a few years. fraud detection, voice analytics, enterprise search. the kind of stuff that pages you at 3am when something breaks.

honestly what keeps me going is when an agent you built solves something you never explicitly told it to do. that feeling never gets old.

what i'm working on right now:

  • multi-agent systems that don't fall apart when you chain them
  • RAG pipelines that actually return relevant results
  • writing about what i learn every day, check it out here
 coding

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featured projects  rocket

Agentic AI Workflows
8 specialized AI agents with LangChain + OpenAI function calling. multi-agent orchestration with planning loops and guardrails. the project i'm most excited about right now.

RAG Enterprise Search
production retrieval pipeline over 2TB+ data. hybrid dense+sparse search with FAISS and BM25, cross-encoder re-ranking. deployed on AWS SageMaker.

Voice AI Platform
real-time voice infrastructure handling 500+ concurrent calls. WebSockets, Kafka, VAD, streaming STT. built the sentiment analysis piece from scratch.

LLM Fine-Tuning LoRA
fine-tuning LLaMA and Mistral with LoRA/QLoRA/PEFT. 40% cheaper than hosted APIs. includes the full training loop, data pipeline, merge + quantize scripts.

RLHF LLM Optimization
full RLHF pipeline: reward model with Bradley-Terry loss, PPO trainer with KL scheduling, DPO as an alternative. 68% win rate on eval, 96% safety compliance.

Sentinel Fraud Detection
ensemble XGBoost + neural net with 650+ engineered features. Redis-backed real-time velocity scoring, SHAP explainability, Kafka alert routing.

view all repositories

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tech stack

not going to pretend i use everything equally. here's what i actually reach for:

tech stack

the full picture (click to expand)
daily drivers Python, PyTorch, FastAPI, Docker, Git, VS Code
LLM and GenAI LangChain, LlamaIndex, HuggingFace Transformers, vLLM, PEFT/LoRA/QLoRA
data and vector FAISS, ChromaDB, Pinecone, PostgreSQL, MongoDB, Redis, Kafka, Elasticsearch
cloud and MLOps AWS (SageMaker, Bedrock, Lambda, ECS), GCP Vertex AI, Azure OpenAI
ML frameworks TensorFlow, scikit-learn, XGBoost, LightGBM, ONNX
infrastructure Kubernetes, Terraform, GitHub Actions, MLflow, Weights & Biases

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github stats

github stats streak stats

top languages

trophies

trophies

contribution graph

contribution graph

my github contributions eating themselves

contribution snake

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recent writeups

i write about what i'm building and learning. nothing polished, more like notes to my future self that happen to be public.

Ai Safety And Alignment Engineering

Ai Safety And Alignment Engineering
2026-05-31

 Real Time Model Serving With Gpus

Real Time Model Serving With Gpus
2026-05-30
Multi Agent Ai Orchestration Patterns

Multi Agent Ai Orchestration Patterns
2026-05-29

 Automated Machine Learning For Computer Vision

Automated Machine Learning For Computer Vision
2026-05-27

📚 View all articles →

recent activity

💬 Commented on YOLOE Visual Prompt based Classification in ultralytics/ultralytics (2026-06-02)

💬 Commented on MCP connected_account_urls use plural query parameter reject in ComposioHQ/composio (2026-06-02)

💬 Commented on providers/index.ts — 429 triggers instant fallback with no R in superagent-ai/superagent (2026-06-02)

💬 Commented on [RFC][Serve] LongPoll consumer resilience under ServeControl in ray-project/ray (2026-06-02)

💬 Commented on llama-factory-0.9.5版本qwen3.5微调报ValueError: Processor was not in hiyouga/LlamaFactory (2026-06-02)

💬 Commented on issue: 0.9.5 -> 0.9.6 Model-attached skills are injected int in open-webui/open-webui (2026-06-02)

⭐ Starred dyad-sh/dyad (2026-06-02)

⭐ Starred microsoft/mcp (2026-06-02)

what i'm reading lately

stuff i've been digging into recently. mostly papers, blog posts, and rabbit holes that kept me up too late.

🔬 Graph RAG and Knowledge Graphs for LLMs

🔬 LLM Fine-Tuning at Scale with LoRA

🔬 AI Safety and Alignment Engineering

🔬 Edge AI and TinyML

🔬 Explainable AI (XAI) for Deep Learning Models

🔬 Real-time Model Serving with GPUs

code snippets

📌 Agent Tool Registry with Dynamic Discovery — Production Pattern (Python) (2026-06-02)

📌 Batch Inference Pipeline with Progress Tracking — Production Pattern (Python) (2026-06-02)

📌 Batch Inference Pipeline with Progress Tracking — Production Pattern (Python) (2026-06-01)

🤖 Profile auto-updated on 2026-06-02 16:49 UTC

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if you made it this far, you should probably just say hi

connect on linkedin   follow on github

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  1. Voice-AI-Platform Voice-AI-Platform Public

    Real-time voice AI infrastructure — 500+ concurrent calls, WebSockets, Apache Kafka, gRPC/C++ with CUDA. Speech-to-text, sentiment analysis, sales insights.

    Python

  2. Agentic-AI-Workflows Agentic-AI-Workflows Public

    Production AI Agents for enterprise automation — 8+ specialized agents using LangChain, OpenAI function calling, and FastAPI. Multi-agent orchestration, tool use, planning loops, guardrails.

    Python

  3. BiiView-Object-Detection BiiView-Object-Detection Public

    Real-time object detection with Meta AI Segment Anything Model (SAM) — 90% accuracy across 11M+ images and 1.1B+ segmentation masks.

    Python

  4. Digital-People-Platform Digital-People-Platform Public

    Hyper-realistic talking avatars — SadTalker lip-sync + Microsoft SpeechT5 TTS + OpenAI conversational AI. 70% realism improvement.

    Python

  5. LLM-Fine-Tuning-LoRA LLM-Fine-Tuning-LoRA Public

    Fine-tuning LLaMA-2, Mistral with LoRA, QLoRA, PEFT — 40% cost reduction vs hosted APIs. VLLM serving with CUDA optimization on AWS SageMaker.

    Python

  6. RAG-Enterprise-Search RAG-Enterprise-Search Public

    Production RAG pipeline — enterprise knowledge retrieval across 2TB+ data using LangChain, FAISS, ChromaDB, PG-Vector with cross-encoder re-ranking. Deployed on AWS SageMaker.

    Python