Applied AI Engineer & Qiskit Advocate
Data Scientist at AIONOS building production RAG systems, LLM applications, and hybrid quantum-classical optimization solutions. Published researcher with 7 papers and international hackathon recognition.
About Me
Data Scientist at AIONOS and Technical Lead at Beerantum, working across LLM engineering and quantum optimization. My day-to-day is building production RAG systems: embedding pipelines, hybrid retrieval (BM25, FAISS, Qdrant), and advanced rerankers including cross-encoders and BGE. KnowledgeOS, my flagship open-source project, spans the full retrieval stack from raw ingestion to benchmarked agentic retrieval.
On the quantum side, I work on hybrid quantum-classical optimization for logistics, aviation, and supply chain problems, with published research and industry work through the QuBri Initiative. My Physics background (MSc, NSUT) shapes how I think about both domains. Optimization at scale is an energy-landscape problem regardless of whether the solver is a QUBO on annealing hardware or a loss surface in PyTorch. The interesting engineering is always in the composition.
Core Skills
Recognition & Awards
Qiskit Fall Fest 2025
Winner
QIntern 2025
First Team Award
Berlin Kipu Quantum Hackathon 2026
3rd Place Winner
Projects
Production systems, research code, and applied AI across LLM engineering and quantum optimization.
KnowledgeOS: Production RAG Platform
A modular, benchmark-driven retrieval platform built from first principles across 6 milestones. Covers the complete pipeline: 5+ embedding strategies, hybrid indexing (BM25, FAISS, Qdrant, RAPTOR), and 8 retrieval strategies including multi-hop, Self-RAG, CRAG, and agentic ReAct. Benchmarked end-to-end: CrossEncoder reranker matches agentic recall at 72x lower latency.
recall@1 = 1.000 | CrossEncoder at 190ms/query | 788x L1 cache speedup
Finetune-Pipeline
End-to-end domain SLM fine-tuning platform. Point at any document corpus, generate synthetic training data, fine-tune via QLoRA on Kaggle, export to GGUF, deploy with Ollama. Switch clients by editing one config file.
Sentinel-AI
7-layer adversarial defense framework for LLM applications. Detects indirect prompt injection, token smuggling, sandbox escapes, social engineering, and RAG exploitation. 50-test suite, 0% false positives on legitimate requests.
90% detection rate | 0% false positives
AgentPitch: Agentic RFP Automation
End-to-end agentic proposal automation for IT services firms. Auto-analyzes RFPs and generates tailored proposals. 68/68 tests passing, FastAPI backend with a production-ready UI.
68/68 tests passing
Clinical-Doc-AI
Context-aware, explainable clinical NLP system for automated medical document understanding. Designed around real-world healthcare workflows with full explainability for regulatory alignment.
Quantum Water Pollutant Classifier
Hybrid Quantum-Classical Neural Network identifying contaminant ions in water using experimental photonic sensor data. Linked to published paper (Jun 2026).
96.67% accuracy on IBM quantum hardware
Quantum-Classical Stock Forecaster
Hybrid Quantum-Classical RNN (LSTM + VQC) for stock index closing price prediction, leveraging a variational quantum circuit for enhanced feature learning.
Publications
7 publications across quantum optimization, hybrid quantum-classical systems, and quantum hardware experiments.
Quantum-Inspired Optimization for Robust Aircraft Cargo Loading Using a QUBO Formulation
Rudraksh Sharma, Ravi Katukam, Arjun Nagulapally
Reformulates aircraft cargo loading as a QUBO problem encoding payload maximization and structural safety. Classical heuristics produce fragile solutions near operational limits; the quantum-inspired approach yields robust placements under perturbation.
Bridging the Linear-Quadratic Gap: A Quantum-Classical Hybrid Approach to Robust Supply Chain Design
Rudraksh Sharma, Ravi Katukam, Arjun Nagulapally
Addresses the trade-off between demand coverage and facility overlap in urban logistics. Tested on a high-fidelity Delhi NCR dataset; hybrid quantum-classical solver outperforms classical optimization on the Pareto frontier of demand capture vs. overlap risk.
Symmetry-Preserving Variational Quantum Simulation of the Heisenberg Spin Chain on Noisy Quantum Hardware
Rudraksh Sharma
Investigates physics-informed ansatz design for VQE on NISQ devices. Symmetry-preserving circuits outperform hardware-efficient baselines on IQM Garnet hardware, validated against exact diagonalization.
Get in Touch
Open to discussing new projects, research collaboration, or freelance opportunities.