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𝗔𝗜 / 𝗡𝗟𝗣 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 | 𝗥𝗔𝗚 & 𝗟𝗟𝗠 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 | 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵-𝗕𝗮𝗰𝗸𝗲𝗱 𝗔𝗜 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 I am an AI and NLP Engineer and published research paper author, specializing in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and applied NLP systems. My work focuses on accuracy, evaluation, and real-world deployment, particularly for domain-specific and low-resource language applications. I work at the intersection of research and production engineering—designing AI systems grounded in peer-reviewed methods, validating them with rigorous evaluation, and delivering production-ready solutions rather than experimental demos. I am the winner of the AIDEA National-Level AI Hackathon, where my project NepSAUL was recognized for technical depth and real-world impact. The project was subsequently selected for angel investment seed funding, validating its practical and commercial viability. 𝐑𝐄𝐒𝐄𝐀𝐑𝐂𝐇 & 𝐏𝐔𝐁𝐋𝐈𝐂𝐀𝐓𝐈𝐎𝐍𝐒 (𝐀𝐔𝐓𝐇𝐎𝐑) • Profanity and Offensiveness Detection in Nepali Social Media Using Bi-directional LSTM Models 21st International Conference on Natural Language Processing (ICON 2024) • Evaluating Sentence Embedding Models for Nepali Sentiment Analysis National Conference on Computer Innovation 2025 • Retrieval-Augmented Generation Framework for the Nepali Legal Domain Question Answering (Under Review) 𝐂𝐎𝐑𝐄 𝐄𝐗𝐏𝐄𝐑𝐓𝐈𝐒𝐄 𝗥𝗔𝗚 & 𝗟𝗟𝗠 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 • Designed and implemented NepSAUL using 10,000+ real court case documents • Hybrid retrieval using BM25 + dense embeddings • Achieved 91% Precision@1 in low-resource legal data • Grounded generation with LLM-as-Judge and expert review 𝗡𝗮𝘁𝘂𝗿𝗮𝗹 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 • Sentiment analysis, text classification, profanity detection • Created and annotated 11,000+ real-world Nepali samples • Noisy, multilingual, domain-specific data handling 𝗠𝗟 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 & 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 • Python, FastAPI, production inference APIs • Research to production model transition • Maintainable and evaluation-driven architectures 𝐓𝐄𝐂𝐇𝐍𝐈𝐂𝐀𝐋 𝐒𝐊𝐈𝐋𝐒 • Python, C++, JavaScript • LangChain, Hugging Face, Multilingual BERT, Bi-LSTM • BM25, FAISS, Pinecone • TensorFlow, Keras, Scikit-learn • FastAPI, Flask 𝐖𝐇𝐘 𝐂𝐋𝐈𝐄𝐍𝐓𝐒 𝐂𝐇𝐎𝐎𝐒𝐄 𝐌𝐄 • Research-backed engineering decisions • Clear and transparent communication • Production-focused system design • Honest feasibility assessment 𝐀𝐕𝐀𝐈𝐋𝐀𝐁𝐈𝐋𝐈𝐓𝐘 If you are building a high-accuracy RAG system, LLM-powered research tool, or NLP pipeline using real-world data, I am available to review requirements and propose a technically sound architecture.

🌍 GIS Analyst & GeoAI Specialist Transforming complex geospatial data into intelligent, real-world solutions using AI and remote sensing I once helped a community with rapid damage assessment during an earthquake, which enabled stakeholders to make instant decisions. That moment solidified my belief that GIS, combined with AI, can protect lives and drive smart decisions. As a GIS & GeoAI Research Associate with 2+ years of hands-on experience, I specialize in solving environmental, agricultural, and infrastructure challenges through intelligent mapping and data-driven models. From crafting high-precision land cover maps to building wildfire prediction models and UAV-based crop yield estimators, I fuse classic GIS with modern AI for results that matter. ✅ Core Strengths Spatial analysis, remote sensing, and image classification (UAV, satellite) Land use/land cover mapping, semantic segmentation GeoAI: Machine learning & deep learning for geospatial tasks Disaster modeling (flood, wildfire, earthquake damage) Geospatial data visualization & decision support systems Python scripting & automation (ArcPy, GEE APIs) 🛠️ Tools & Platforms GIS & RS: ArcGIS Pro, QGIS, Google Earth Engine, ENVI AI/ML: Python (scikit-learn, TensorFlow, Keras), PyTorch Visualization: Power BI, Tableau, Folium, Leaflet Web & App: ArcGIS Online, Flutter (for geospatial apps) Modeling: LSTM for weather/forecast modeling 🌱 Why Collaborate with Me? Innovation Meets Impact: I bring AI-powered insight to traditional GIS workflows, making solutions smarter and more scalable. Field-Tested Expertise: From rice yield prediction to plant disease diagnosis and disaster response — my models are built for real-world deployment. Geospatial Visionary: Passionate about crafting geographically explicit AI architectures that truly “understand” space and place. Dependable & Collaborative: Communication-focused and outcome-driven — I thrive in partnerships aiming for impact. Let’s build solutions that not only map the world but make it better. Whether you're a researcher, NGO, or organization tackling environmental or agricultural challenges, let’s collaborate! 🌾📍🌪️

Hi, I'm Bikash a Machine Learning Engineer who takes AI systems from prototype to production: fine-tuned LLMs, retrieval-augmented generation, inference APIs, and NLP/audio pipelines built to actually run in real applications, not just notebooks. I help businesses move from AI ideas to working software: training and fine-tuning models, building APIs, optimizing inference, integrating ML into applications, and deploying systems that are practical, reliable, and easy to maintain. What I can help you with: 1. LLM fine-tuning and retrieval-augmented generation (RAG) systems, with structured, citation-grounded outputs 2. Machine learning models for classification, prediction, scoring, and decision support Production ML workflows using Python, TensorFlow, PyTorch, Scikit-learn, FastAPI, Docker, and cloud deployment 3. NLP systems for summarization, translation, semantic search, recommendations, and text-to-speech 4. Audio AI systems, including sound classification, scoring logic, temporal smoothing, and lightweight inference APIs 5. Backend integration for ML products using REST APIs, FastAPI, Flask, databases, and cloud deployment What I've actually built: 1. Khabar AI: an end-to-end AI news platform using 36+ RSS sources, mBART summarization, English-to-Nepali translation, Qdrant semantic search, recommendations, newsletters, and Piper TTS. 2. MarketGyan: a Nepal-focused financial NLP system extending Khabar AI into Nepal Stock Exchange (NEPSE) market analysis. I built a 500-row bilingual benchmark and fine-tuned a Qwen3.5-9B LoRA adapter for structured, evidence-grounded extraction, reaching 1.000 structured-output validity and 0.796 relevance macro-F1, then deployed a retrieval-augmented generation runtime delivering cited, disclaimer-gated market answers. 3. Laughter Detection & Scoring System: a cloud-deployed audio classification API using YAMNet embeddings, TensorFlow Lite optimization, FastAPI, and Google Cloud Run. I built probability aggregation, temporal smoothing, and noise-aware thresholds, reaching 93.33% accuracy, 0.909 F1, 1.0 recall, and 0.95 balanced accuracy. 4. EEG Epilepsy Detection System: a deep learning signal-classification pipeline for EEG-based epilepsy detection, reaching 99.47% classification accuracy in academic evaluation. 5. Text-to-Speech System: a Nepali TTS project using Coqui TTS, Glow-TTS, HiFi-GAN, Gradio, and Librosa, reaching 4.07/5.0 MOS with about 2-second CPU inference per 10 words. I'm also currently a Research Intern at Kathmandu University's Information and Language Processing Research Lab (ILPRL), working on active learning and low-resource speech ASR so I stay current with the field, not just applied delivery work. Core skills and tech stack: Machine Learning: production ML, model serving, inference APIs, ML pipelines, deep learning, model evaluation, active learning AI/NLP/LLMs: semantic search, summarization, translation, speech synthesis, audio classification, LLM fine-tuning (LoRA), retrieval-augmented generation Frameworks: Python, TensorFlow, PyTorch, Scikit-learn, Keras, Pandas, NumPy, Librosa, XLM-R, Qwen Deployment: FastAPI, Flask, REST APIs, Docker, TFLite, Google Cloud Run, vLLM, Gradio, Git, Linux Databases and search: SQL, PostgreSQL, MongoDB, SQLite, Qdrant vector search Certifications: Google Cloud Computing Foundations, Dataquest Data Scientist in Python, Dataquest Deep Learning in TensorFlow I focus on clean code, clear communication, measurable results, and AI systems that can actually be integrated into real applications. I also have fluent English proficiency, with IELTS 7.0 / CEFR C1. If you need a Machine Learning Engineer who can turn your idea into a working model, API, or deployable AI system, send me a message and I'll help you plan the best approach.

I am Machine Learning Engineer who has done multiple projects related to Computer Vision, NLP, deep learning, and Data Analysis. • Deep learning: Keras, Tensorflow, Pytorch, MxNet. • Machine learning: Scikit-earn. • Computer Vision: OpenCV, Pillow, SciPy, Scikit-Image. • NLP: Spacy, NLTK, Textblob. • Data cleaning, manipulation, feature extraction, selection, and preprocessing with Pandas, and Numpy. • Data visualization with Matplotlib, Seaborn, and Plotly. • Experienced with Linux and Shell Scripting. • Experienced with Git.

Transforming Complex Data Into Business Solutions That Drive Real Results 3+ Years delivering production-ready AI systems for businesses worldwide 96% Client Satisfaction with rapid, business-first delivery approach Academic Research Background with proven track record in technical documentation What I Deliver For Your Business: Computer Vision Systems – YOLO v11/8, real-time object detection, fall detection systems, security monitoring AI/ML Solutions – Custom models, RAG pipelines, predictive analytics, automation workflows Academic Research & Writing – Literature reviews, methodology design, technical documentation, citation formatting Web Scraping & Automation – Large-scale data extraction, API integrations, automated reporting systems Data Engineering – Database optimization, ETL pipelines, data visualization, statistical analysis 🛠️ Technical Expertise: Languages: Python, JavaScript, SQL AI/ML: PyTorch, TensorFlow, OpenAI, Hugging Face, LangChain Computer Vision: YOLO, MediaPipe, OpenCV, Detectron2 Cloud & Deployment: Docker, AWS, GCP, CI/CD pipelines Research Tools: LaTeX, academic databases, statistical software 📊 Recent Client Wins: Rooftop Detection System → 50K+ solar installations mapped per hour using aerial imagery Academic Literature Review → 96% accuracy on complex regulatory research for policy institute E-commerce Data Pipeline → Automated scraping system processing 100K+ products daily Fall Detection App → Real-time monitoring system selected by NAST Young Scientist Program 🎓 Why Clients Choose Me: Academic Precision – Research-grade methodology meets business practicality Clear Communication – Regular updates, milestone tracking, zero surprise costs Rapid Prototyping – Working solutions in days, not weeks Long-term Thinking – Code built for scale, maintenance, and future growth Ready to turn your data challenges into competitive advantages? Let's discuss how I can help you achieve measurable results with cutting-edge AI and meticulous research.

📌 Machine Learning Engineer | Data Engineer | Data Analytics Specialist Are you looking for a data expert who can bridge the gap between heavy infrastructure, advanced modeling, and executive-ready insights? I engineer scalable data pipelines, deploy predictive machine learning models, and build high-fidelity analytics ecosystems that turn raw, complex information into automated business intelligence. With a background rooted in rigorous computer engineering principles, I don't just build charts—I design the underlying architecture to make sure your data flows seamlessly, trains accurately, and drives measurable growth. --- 🧠 Machine Learning & Predictive Modeling I design, evaluate, and deploy predictive systems that uncover hidden non-linear interactions in data and automate complex decision-making frameworks. • End-to-End ML Pipelines – Implementing robust feature engineering, scaling, and validation strategies to prevent overfitting. • Advanced Regressors & Classifiers – Training and tuning baseline statistical models against advanced ensemble algorithms (Random Forests, XGBoost). • Model Evaluation & Optimization – Leveraging rigorous mathematical validation metrics (R², MAE, RMSE, Confusion Matrices) to prove performance margins. • Production Deployment – Packaging models (.pkl) and designing backend routing layers (Flask API architectures) to deliver live, low-latency predictions. • Tools I Use: Python, Scikit-Learn, Pandas, NumPy, XGBoost, LightGBM, Flask. --- ⚙️ Data Engineering & Pipeline Infrastructure Building the robust, reliable backend architecture required to process massive datasets and establish a clean, centralized source of truth. • ETL/ELT Pipeline Automation – Extracting, transforming, and staging disparate, unformatted, or corrupted source records. • Database Design & Schema Optimization – Engineering relational architectures and writing highly optimized, high-performance SQL scripts. • Advanced Data Cleaning & Governance – Implementing production-grade missing value imputation, text normalization, and structural dimensionality alignments. • Out-of-Core Processing – Designing lazy-evaluation pipelines to run operations smoothly over out-of-memory, multi-gigabyte files. • Tools I Use: Microsoft SQL Server (MSSQL), PostgreSQL, Python, Polars, Dask, Git/GitHub. --- 📊 Data Analytics & Business Intelligence (BI) Translating complex algorithmic outputs and underlying database models into clear, interactive corporate stories. • Executive-Ready Dashboards – Building cross-functional visual stories featuring advanced layout designs and dynamic action filters. • Exploratory Data Analysis (EDA) – Performing deep-dive statistical analysis to isolate operational trends, anomalies, and compounding risk factors. • Key Performance Indicators (KPIs) – Mapping complex model metrics back into clear, financial outcomes that stakeholders can act on immediately. • Tools I Use: Tableau Desktop/Public, Power BI, Advanced Excel. --- 💡 Why Partner With Me? • Full-Stack Data Literacy: I understand how a single corrupted character in a database affects a machine learning model’s accuracy and an executive's final dashboard. • Algorithmic Transparency: I don't treat ML as a black box; I clearly articulate why models behave the way they do based on the math. • Production-Grade Habits: Every script I write is version-controlled, clean, document-heavy, and built with secure environment variable practices. --- 📩 Let’s Connect Whether you need to scale up a memory-mapped processing pipeline, train a predictive model on messy real-world data, or build an interactive Tableau command center, I am ready to deliver. Send over a message, and let’s discuss how we can build your next data engine together!