Skip to content
View trohit920's full-sized avatar

Block or report trohit920

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Maximum 250 characters. Please don’t include any personal information such as legal names or email addresses. Markdown is supported. This note will only be visible to you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
trohit920/README.md

Rohit Thakur

AI/ML Engineer  ·  Seoul, South Korea
Agentic LLM systems · Uncertainty & evaluation · Real-time edge vision

Portfolio LinkedIn Email 8+ years


What I do

I build AI systems that survive production, and I spend most of my time on the part people skip: knowing when the model should be trusted.

That means agentic LLM applications with measured evaluation rather than vibes, real-time computer vision tuned to run on edge silicon, and the manufacturing analytics that connect a sensor reading to a business decision.

Currently at eMoldino, building a manufacturing intelligence platform on AWS Bedrock, LangGraph and Snowflake.


Selected work

Project What it does
llm-uncertainty-quantification Token- and sentence-level uncertainty for local LLMs. Semantic entropy via NLI clustering, calibration, risk-coverage, bootstrap CIs. Found a free single forward pass beating 10-sample semantic entropy, and traced exactly why.
CabinSense Real-time in-cabin driver and occupant monitoring. ONNX face detection, 98-point landmarks, head-pose geometry and a multi-task classifier, INT8-quantized for SoC deployment.
Agentic RAG with Guardrails Fully local agentic RAG with hybrid retrieval, NLI groundedness checks, prompt-injection defense and a judge-free evaluation harness.

Numbers I am proud of

33%     lower test MAE on scrap-rate prediction  (16.3% -> 10.9%)
95.5%   fault-detection accuracy, railway pantograph monitoring
99.5%   mAP@50, sorting-robot vision pipeline
20%     manufacturing efficiency gain
12.3%   energy saved via anomaly detection and control
210+    automated LLM evaluation tests in production

Stack

LLM & GenAI LangGraph LangChain Agentic RAG MCP AWS Bedrock DeepEval LLM-as-Judge Guardrails Hybrid retrieval FAISS Chroma

Vision & Edge PyTorch TensorFlow OpenCV YOLOv5/v8 U-Net ONNX Runtime INT8 TensorRT MediaPipe

Data & Platform Snowflake Snowpark Spark Kafka FastAPI Docker Kubernetes MLflow Langfuse Databricks


Background

M.S. Electrical Engineering & Computer Science — Gwangju Institute of Science and Technology (GIST), 2017 · AI and machine learning

Published at ICMLA and KICS. Innovation Award at OpenSysNet, Employee of the Year at Pittasoft. IEEE member.

English (fluent) · Korean (professional working) · Hindi (native)


Open to AI/ML engineering roles — agentic systems, evaluation and reliability, or real-time vision.
trohit920.github.io

Pinned Loading

  1. ADAS-Features ADAS-Features Public

    Advance Driver Assistance System

    C++ 1

  2. Drowsiness-Detection-using-Head-Movement Drowsiness-Detection-using-Head-Movement Public

    Driver Drowsiness Detection

    C++ 2 2

  3. Custom_Yolo_Object_Detection Custom_Yolo_Object_Detection Public

    Training Tensorflow based Yolo Object Detection on Custom Data

    Python 1

  4. Image-Segmentation-Training-and-Testing Image-Segmentation-Training-and-Testing Public

    Python

  5. llm-uncertainty-quantification llm-uncertainty-quantification Public

    Token-level and sentence-level uncertainty estimation for open-source LLMs: semantic entropy, calibration, and uncertainty-gated decoding. Runs on a single 8GB GPU.

    Jupyter Notebook