I’m an AI Engineer and a Data Science graduate student interested in building intelligent systems that are not only accurate, but useful, measurable, and reliable in the real world.
- 🎓 M.Sc. Data Science at TU Braunschweig, Germany
- 🧠 B.E. Artificial Intelligence & Machine Learning from Dayananda Sagar College of Engineering, Bengaluru
- 📄 3 peer-reviewed publications across IEEE and Springer venues
- 🏆 3× hackathon winner
- 📍 Based in Braunschweig, Germany
- 🎯 Open to AI Engineer, ML Engineer and Data Scientist opportunities
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Multi-hop retrieval, tool calling, |
Time-series forecasting, computer vision, |
FastAPI services, Docker, cloud AI, |
Python • C++ • SQL
PyTorch • TensorFlow • Scikit-learn • OpenCV
LangChain • LangGraph • Tavily
FastAPI • Docker • Azure • Git • Linux
Neo4j • ChromaDB
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Bilingual German and English solar advisory assistant combining a deterministic calculation engine with an LLM language layer. Retrieval is grounded in regulatory and tariff documents, with answers traced back to their original sources.
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Fraud detection and audit assistant designed around source-traceable evidence, allowing reviewers to connect conclusions directly to supporting documents. A deterministic rule core performs detection, while the LLM is confined to explanation and summarisation.
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Three-stage Alzheimer's drug discovery pipeline combining bioactivity and toxicity prediction, GRU-based reinforcement-learning molecule generation, and AlphaFold-based interaction modelling. Achieved 86% prediction accuracy, 84% SMILES validity, and 75% interaction-modelling accuracy.
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Human-in-the-loop verification layer over YOLOv8 using RemoteCLIP embeddings, a logistic-regression gate, and a VLM escalation path for low-confidence detections. The project documented a significant negative result where synthetic proposals actively misled real-data evaluation.
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Working Student · AI & Data Solutions (AIDA)
📍 Hannover, Germany • May 2025 → May 2026
- Built production AI and RAG workflows for industrial analytics and raw-material price modelling.
- Developed FastAPI orchestration services exposing REST APIs for downstream applications.
- Integrated Azure AI Foundry with web grounding into LLM workflows.
- Automated analytical workflows, reducing approximately 6 man-weeks of manual analyst effort.
- Optimised inference workflows through prompt chaining and model-selection experiments.
Artificial Intelligence Intern
📍 Bengaluru, India • Feb 2024 → May 2024
- Built an LSTM predictive-maintenance system for an Antenna Control Servo System.
- Achieved MAE ≈ 0.06.
- Co-authored and presented the resulting research at IEEE SPACE 2024.
IEEE Space, Aerospace and Defence Conference (SPACE 2024) · ISRO & DRDO
N. Satish, S. Menon, D. Arora, M. P. Devi, S. Santhalakshmi
📗 Innovating Drug Design for Alzheimer's Disease via Reinforcement Learning for Enhanced Molecular Generation
4th International Conference on Innovations in Computational Intelligence and Computer Vision · Springer, Scopus indexed
N. Satish, M. Bukapindi, S. Kuntnal, G. Akhil, V. P. Malagi
📙 Optimizing Traffic Management Through Density-Driven Dynamic Traffic Signaling and Emergency Vehicle Prioritization Using Audio and Video
7th International Conference on Innovative Computing and Communication · Springer, Scopus indexed
N. Satish, M. Bukapindi, S. Kuntnal, G. Akhil, V. Akash, S. K. Vasudevan, T. S. Murugesh
Interested in AI engineering, intelligent systems, retrieval, LLMs and applied machine learning? I’m interested in all of these and would love to talk or have a conversation.
Portfolio |
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GitHub |
Made with ❤️ by Nishank