Hire the Best Machine Learning Engineers
in Nepal

I'm a Top-Rated AI Engineer specializing in enterprise-grade AI solutions, multi-agent systems, scalable backend architecture and data engineering solution. With 1+ years of focused AI development experience and a proven track record at multinational companies, I transform complex business requirements into intelligent, production-ready applications that drive measurable results. Core Specializations: Agentic AI & Multi-Agent Systems Expert in building cutting-edge agentic applications using LangGraph, CrewAI, and Model Context Protocol (MCP). I develop custom multi-agent architectures tailored to specific business use cases, enabling intelligent automation and decision-making systems. Advanced RAG Applications Built 10+ highly secured production RAG systems with self-RAG and adaptive architectures. Specialized in implementing evaluation frameworks and optimizing retrieval accuracy for enterprise-scale applications across multiple industries. LLM Fine-tuning & Cost Optimization Proven expertise in instruction fine-tuning (GPT-4o mini), custom model training, and cost-effective model replacement strategies. Successfully reduced client AI costs by up to 40% while maintaining performance through strategic open-source model implementation. Enterprise Backend Development Master-level proficiency in Python (FastAPI), database design with Alembic versioning, and cloud deployment with AWS. Built scalable systems handling enterprise-level workloads with robust CI/CD pipelines. Technical Arsenal: AI/ML Frameworks: LangChain, Langgraph, Ollama, VLLM, Vector Databases: Qdrant, FAISS, ChromaDB Data Processing: PDF, DOCX, PPTX, Excel, images (OCR), web scraping Models: OpenAI, Claude, Gemini, fine-tuned open-source models MLOps: Docker, CI/CD pipelines, Airflow, model versioning, evaluation frameworks Computer Vision & NLP: Custom model training, Stable Diffusion, OCR optimization Backend: PostgreSQL, MySQL, MongoDB, database versioning with Alembic Cloud & DevOps: AWS, containerization, Scalable deployment Big Data Processing: Apache spark, pyspark, Airflow, Databricks, SQL, DBT Reinforcement Learning: Stable Baselines, Gymnasium Time series prediction: SARIMA, ARIMA, FBProphet 🧠 Why Choose Me: Proven Excellence: Top-rated freelancer with enterprise-level project experience and multinational company background and startup companies Innovation-Driven: Stay ahead of AI trends, implementing cutting-edge technologies like MCP, adaptive RAG, and latest LLM advancements daily Cost-Conscious Solutions: Specialize in building high-performance AI systems that optimize costs without compromising quality Full-Stack Capability: While AI/ML focused, I possess deployment skills and can deliver complete end-to-end solutions Exceptional Collaboration: High availability, flexible timing, proficient with GitHub/GitLab workflows, and always eager to explore new technologies Quality Standards: Write production-grade code, implement proper testing frameworks, and follow industry best practices for maintainable and scalable solution. What I can help you build Production-ready MVPs using LLMs, RAG pipelines, and AI agents from idea to deployment Research-driven AI solutions for new products, including model selection, evaluation, and experimentation Custom AI agents (coding, research, content, automation) using modern agent frameworks Fine-tuned open-source LLMs (SFT / RLHF / RLVR) hosted privately or on-prem Scalable backends & APIs (FastAPI) to integrate AI into real systems Keywords: AI agent, AI developer, Chatbots, AI Automation, Langchain, Langraph, n8n, VAPI, Retell AI, GHL, make,

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 help people use data driven insights to solve real world problems using Machine Learning and Data Science. My services include: data preprocessing, data visualization, data analysis, data extraction, data mining,model training, model creation, model evaluation etc .

𝗪𝗵𝘆 𝗯𝘂𝗶𝗹𝗱 𝗔𝗜 𝘁𝗵𝗮𝘁 𝗼𝗻𝗹𝘆 𝘄𝗼𝗿𝗸𝘀 𝗶𝗻 𝗮 𝗻𝗼𝘁𝗲𝗯𝗼𝗼𝗸? I help businesses turn AI/ML ideas into working, production-ready systems — from data preparation and model training to APIs, GPU inference, backend integration, and deployment. With 7+ years of software engineering experience, I combine AI/ML expertise with strong Python backend and infrastructure engineering to build systems that can actually be used in production. How I Build Real-World AI Systems ➤ 𝗟𝗟𝗠, 𝗥𝗔𝗚 & 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 Build AI assistants and intelligent applications using LLMs, RAG, embeddings, vector search, and structured outputs. I can integrate LLMs into existing applications, build document-based RAG systems, implement semantic search, and create reliable AI workflows around real business data. ➤ 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 & 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 Build AI-powered workflows that combine LLMs, APIs, databases, retrieval systems, and business logic. From tool-calling and structured outputs to multi-step AI workflows, I focus on making AI useful inside existing products rather than building isolated demos. ➤ 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 & 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 Develop and deploy custom deep learning systems for: Image classification Object detection Segmentation Image tracking OCR and handwriting recognition Document intelligence Medical and scientific imaging Using PyTorch, TensorFlow, OpenCV, and Hugging Face, I can take a project from dataset preparation and training through evaluation, optimization, and deployment. ➤ 𝗣𝘆𝘁𝗵𝗼𝗻 𝗕𝗮𝗰𝗸𝗲𝗻𝗱 & 𝗔𝗜 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 Build the backend infrastructure required to turn AI models into usable products. I work with Python, FastAPI, Django, Flask, REST APIs, PostgreSQL, MySQL, Redis, RabbitMQ, and Celery to build asynchronous and scalable AI services. Whether you already have a trained model or need the complete AI backend, I can connect the pieces into one working system. ➤ 𝗚𝗣𝗨 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 & 𝗠𝗼𝗱𝗲𝗹 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 Optimize AI workloads for real-world inference and training environments. This includes GPU-based inference, model optimization, batching, asynchronous processing, resource utilization, and deploying ML workloads as scalable services. ➤ 𝗠𝗟𝗢𝗽𝘀 & 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗔𝗜 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 Take models from development to production using: Docker • Linux • CI/CD • GPU infrastructure • ML pipelines • Model serving • Monitoring • Scaling I focus on reproducible training, reliable inference, clean APIs, and infrastructure that can support the system after the initial prototype. Why Clients Work With Me 𝗘𝗻𝗱-𝘁𝗼-𝗘𝗻𝗱 𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴: I can work across the model, backend, API, infrastructure, and deployment layers. 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗙𝗼𝗰𝘂𝘀𝗲𝗱: I care about reliability, latency, scalability, and maintainability — not just model accuracy. 𝗦𝘁𝗿𝗼𝗻𝗴 𝗣𝘆𝘁𝗵𝗼𝗻 𝗕𝗮𝗰𝗸𝗲𝗻𝗱: AI systems still need solid software engineering, APIs, databases, and asynchronous processing. 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗠𝗟: I work with real datasets, evaluation metrics, model optimization, and deployment constraints. Recent AI/ML Work 𝗠𝗮𝗹𝘄𝗮𝗿𝗲 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀: Built ML-powered malware analysis and threat intelligence systems combining static analysis, sandboxing, reverse engineering, and machine learning. 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻: Developed deep learning pipelines for image analysis, detection, segmentation, and tracking. 𝗡𝗲𝗽𝗮𝗹𝗶 𝗢𝗖𝗥: Worked on handwritten Nepali word recognition using transfer learning and transformer-based OCR models. 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗔𝗜: Built pipelines for extracting structured information from complex documents and images. 𝗠𝗟 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲: Built asynchronous ML processing systems using Python, Redis, RabbitMQ, Celery, Docker, and GPU infrastructure. 𝗟𝗟𝗠/𝗥𝗔𝗚: Build and integrate LLM-powered applications, retrieval pipelines, AI APIs, and intelligent document workflows. If you have a dataset, model, AI prototype, API, or technical specification, I can help turn it into a working production system.