AI Engineer specializing in AI, LLMs, and Quantum-AI systems, with experience in building energy-efficient Transformer architectures, neuromorphic models. Currently contributing to a DST-sponsored project at NIT Jaipur o…
AI Engineer specializing in AI, LLMs, and Quantum-AI systems, with experience in building energy-efficient Transformer architectures, neuromorphic models. Currently contributing to a DST-sponsored project at NIT Jaipur on AI-assisted robotics and battery management systems, driving innovation in sustainable and high-performance computing.
Applied machine learning techniques and statistical methods to analyze sensor-based datasets (e.g., battery performance data), identifying patterns in charging/discharging cycles and improving predictive modeling of system behavior.
Developed ETL pipelines for DOBOT CR3 and MG400 test benches, producing clean datasets for analytics.
Coordinated cross-functional integration of battery hardware with automation scripts, enabling synchronized experiments and consistent data acquisition.
Developed ETL pipelines for DOBOT CR3 and MG400 test benches, producing clean datasets for analytics. Coordinated cross-functional integration of battery hardware with automation scripts, enabling synchronized experiments and consistent data acquisition. Video Demonstration – Robotics Automation Setup - https://youtu.be/4Aygwmuk-TU.
Contributed to RLHF, SFT and SRT projects (Hopper, Cypher Evals, Matcha Stem, Pref Ranking, Extensions V2 Log), enhancing generative AI training through code evaluation and task curation.
Evaluated AI model outputs and engineered comprehensive feedback reports to advance large language model accuracy and reliability across diverse task sets.
Contributed to RLHF, SFT and SRT projects enhancing generative AI training...
Developed an autonomous financial analysis application, Q-OptiFolio, to perform end-to-end cryptocurrency portfolio optimization using specialized AI agents. Implemented a hybrid classical–quantum workflow using QAOA (IBM Qiskit) to solve the combinatorial asset-selection problem, achieving a Sharpe ratio of 0.33, 4.9 % expected annual return, and 8.7 % volatility on an ETH-ADA-DOGE portfolio. Tech Stack: CrewAI, Qiskit, Litestar, React/Next.js , Groq (LLM Inference), OpenAI GPT-OSS-120B, CoinGecko API, Tailwind CSS.
Architected a state-of-the-art GraphRAG pipeline that integrates semantic search with knowledge graph traversal (using NetworkX), significantly improving reasoning capabilities across complex, unstructured 10-K financial reports. Engineered a hybrid retrieval system combining Qdrant vector search with LLM-based entity–relationship extraction, delivering citation-backed answers and achieving 80% RAGAS factual accuracy. Added a Natural Language-to-Query module enabling automated, accurate Plotly data visualizations based on user intent. Built an interactive Streamlit chat app with real-time token streaming and optimized 4-bit quantization (BitsAndBytes) to efficiently run Mistral-7B. Technologies: LangChain, Prompt Engineering, LlamaIndex, Hugging Face, Mistral-7B , Qdrant, Transformers, Streamlit, PyTorch.
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