München, Deutschland · LinkedIn · prateekgaur@gmx.de
I am an Applied ML Engineer with a background that grew from practice: Mechanical Engineering (B.Tech.) → Battery Systems & Energy Engineering (M.Sc. TU Berlin) → Machine Learning & AI Engineering
This combination is rare. I don't just understand Python, PyTorch, and LLM pipelines — I understand what the data physically means. Battery degradation models, thermal simulations, time-series data from industrial systems: this is the domain knowledge my ML solutions are built on.
Recently, I've been building multi-agent systems with human-in-the-loop review and production-grade computer vision pipelines — bringing the same rigor from battery engineering to agentic AI architecture.
| Area | Tools |
|---|---|
| ML & Deep Learning | Python · PyTorch · scikit-learn · LSTM/RNN · 3D U-Net · XGBoost |
| LLM & Agentic AI | LangChain · LlamaIndex · RAG systems · FAISS/Chroma · local LLMs |
| Multi-Agent & Agentic Systems | A2A protocols, agent orchestration, confidence routing, HITL workflows |
| Battery & Energy | SOC/SOH modelling · BMS · thermal simulation · cell balancing |
| MLOps & Cloud | Azure · CI/CD basics · SQL · REST APIs · Git · reproducible experiments |
| CAD & Simulation | CATIA V5 · Siemens NX · SolidWorks · MATLAB/Simulink · HyperWorks |
End-to-end platform merging extraction, anomaly detection, classification, and decision
agents with a confidence-based human review workflow. Processes records through automated
quality gates, escalating low-confidence cases for review and feeding corrections back into
an RLHF dataset. Full audit trail and live dashboards.
Python Streamlit n8n PyTorch FAISS Ollama scikit-learn HITL
Battery cell defect detection system built on a custom CNN (98.8% test accuracy, 0.988 macro
F1) orchestrated by four cooperating agents — vision, memory, decision, and feedback —
communicating over a typed Agent-to-Agent (A2A) protocol. Includes cross-session memory and
live drift detection.
Python PyTorch OpenCV A2A Protocol Multi-Agent Computer Vision
LSTM and RNN models for State-of-Health estimation and degradation forecasting on real
industrial battery datasets. Benchmarked against Random Forest and SVM baselines.
Python PyTorch LSTM time-series battery
Production-ready Retrieval-Augmented Generation pipeline over internal simulation guidelines.
Integrates local LLMs for data privacy. Semantic search over complex engineering
specifications.
LangChain LlamaIndex FAISS RAG local-LLM
🎓 M.Sc. Energy Engineering — Technische Universität Berlin (2021) Thesis: Custom Battery Cell Balancing Circuit Design Under Thermal Gradient
🎓 B.Tech. Mechanical Engineering — Rajasthan Technical University (2016)
✅ Machine Learning Specialization — DeepLearning.AI / Stanford Online ✅ Battery Management Systems — University of Colorado Boulder (with honours) ✅ Python for Everybody — University of Michigan ✅ CATIA V5 Certified Associate — Dassault Systèmes ✅ Siemens NX Certified Designer
ML Engineer · AI Engineer · Data Scientist · Battery Systems Engineer Open to roles in Munich and remote across Germany
Languages: English (C2) · German (B2+) · Hindi (native)