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I help businesses eliminate repetitive tasks, connect disconnected systems, and scale operations through AI-powered automation and machine learning. If your team is drowning in data entry, switching between tools, or answering the same questions on repeat, I build the systems that fix it. 𝐖𝐡𝐨 𝐈 𝐀𝐦 I am an AI/ML Engineer and Automation Specialist with deep expertise in machine learning, large language models (LLMs), AI agents, workflow automation, and Python development. I don't just write code, I understand business operations, ask the right questions, and deliver production-ready systems that solve real problems. My background spans custom AI/ML solutions, conversational AI, predictive models, backend development, and end-to-end automation pipelines. I have successfully delivered AI-powered platforms for service businesses, agencies, clinics, manufacturing operations, and e-commerce companies. 𝐖𝐡𝐚𝐭 𝐈 𝐃𝐨 𝟏. 𝐀𝐈/𝐌𝐋 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 – Predictive analytics, classification, recommendation systems, anomaly detection, custom ML pipelines, and fine-tuning LLMs (Llama, Mistral, OpenAI) on proprietary data. 𝟐. 𝐋𝐋𝐌 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 & 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 – LLM-powered applications, autonomous AI agents, RAG (retrieval-augmented generation), function calling, and tool use with OpenAI GPT-4o, Claude, Gemini, and Llama. 𝟐. 𝐋𝐋𝐌 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 & 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 – Inbound/outbound voice agents (VAPI, Retell AI, Twilio), AI chatbots for websites, WhatsApp, Slack, and SMS, plus omnichannel AI assistants with unified context. 𝟒. 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 – n8n, Make, and Python-based automations connecting CRMs, databases, email, Slack, and hundreds of other tools. AI-powered workflows using LLMs for classification, extraction, and content generation. 𝟓. 𝐏𝐲𝐭𝐡𝐨𝐧 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 & 𝐁𝐚𝐜𝐤𝐞𝐧𝐝 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 – RESTful APIs, data pipelines (ETL), web scraping, custom automation scripts, and backends for AI applications using FastAPI, Django, or Flask. 𝐑𝐞𝐚𝐥 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 𝐈 𝐇𝐚𝐯𝐞 𝐁𝐮𝐢𝐥𝐭 𝐒𝐨 𝐒𝐩𝐞𝐜𝐢𝐚𝐥 𝐌𝐨𝐛𝐢𝐥𝐞 𝐌𝐞𝐜𝐡𝐚𝐧𝐢𝐜 – AI-infused mobile application and operational platform including dispatch, customer tracking, automated notifications, and backend dashboards. 𝐀𝐥 𝐀𝐫𝐚𝐛𝐢 𝐂𝐨𝐦𝐩𝐚𝐧𝐲 – Dispatch system for operations and maintenance management with real-time job assignment, status tracking, and reporting. 𝐌𝐲𝐬𝐭𝐢𝐜𝐚𝐥 𝐀𝐠𝐞𝐧𝐜𝐲 & 𝐈𝐧𝐭𝐞𝐫𝐨 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 – AI automation and proposal generation systems that reduced proposal creation time from two days to under two hours. 𝐂𝐥𝐢𝐧𝐢𝐜 𝐖𝐡𝐚𝐭𝐬𝐀𝐩𝐩 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 – AI chatbot for appointment booking, prescription reminders, FAQs, and patient follow-ups, fully integrated with their scheduling system. 𝐄-𝐜𝐨𝐦𝐦𝐞𝐫𝐜𝐞 𝐑𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐚𝐭𝐢𝐨𝐧 𝐄𝐧𝐠𝐢𝐧𝐞 – Custom ML model for product recommendations based on browsing and purchase history. Each of these systems typically includes AI/ML models, AI agents, automation pipelines, mobile apps, dashboards, and backend integrations. 𝐌𝐲 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 𝐒𝐭𝐚𝐜𝐤 - Languages: Python (primary), JavaScript/Node.js, SQL - ML & AI Frameworks: TensorFlow, PyTorch, scikit-learn, Hugging Face, LangChain, LlamaIndex - LLMs: OpenAI GPT-4o, Claude, Gemini, Llama, Mistral - Workflow Automation: n8n, Make, Zapier - Voice AI: VAPI, Retell AI, Twilio - Vector Databases: Pinecone, Weaviate, Chroma, FAISS - Data & ETL: Pandas, NumPy, SQLAlchemy, Airflow - Backend: FastAPI, Django, Flask, Node.js - Databases: PostgreSQL, MySQL, MongoDB, Redis - Cloud: AWS (SageMaker, Lambda, EC2), GCP (Vertex AI), Azure, Docker 𝐇𝐨𝐰 𝐈 𝐖𝐨𝐫𝐤 I approach every project with a structured, collaborative process: 1- Discovery – I provide a clear checklist of inputs, decisions, and data needed from you. 2- Design & Build – I configure, develop, and test the system. For ML projects, I start with rapid prototyping. 3- Review & Revise – You test. I revise. We iterate until it's right. 4- Launch – I help you execute a clean cutover with minimal disruption. 5- Documentation – You receive clear documentation on what was built and how to maintain it. 𝐈𝐝𝐞𝐚𝐥 𝐂𝐥𝐢𝐞𝐧𝐭𝐬 - You are a great fit if you: - Are a business owner, operations manager, product manager, or technical founder - Have outgrown manual processes or disconnected tools - Need AI/ML models for predictions, classification, recommendations, or anomaly detection - Want AI voice agents, LLM-powered chatbots, or autonomous AI agents - Need workflow automation across CRMs, databases, and messaging platforms - Require custom software, backend systems, or mobile applications - Want clear communication and on-time delivery 𝐋𝐞𝐭'𝐬 𝐓𝐚𝐥𝐤 If you are ready to bring AI/ML-powered automation into your operations, send me a message or invite me to your project. I will reply within 24 hours with thoughtful questions and a clear path forward.

I help healthcare organizations turn medical records, clinical notes, and research papers into useful AI tools. This includes RAG systems that let teams search documents in plain language, prediction models, and analytics dashboards. I handle the full project myself, from the data work through deployment, including the web application and payment setup when needed. Recent work: HealthDataVitals: a live platform I built and launched that tracks healthcare cost, quality, and performance data. I managed the dashboard, the data pipeline, and payment integration from start to finish. An agentic RAG system that lets teams upload medical documents and ask questions about them in plain language. It automatically detects and removes protected health information from medical files before storing them, so documents stay searchable without exposing patient data. A diabetes prediction system built with ensemble models (Random Forest, XGBoost, and LightGBM) and SHAP explainability, so users can see which factors drove each prediction. I built multiple ways to access it, including a web app, a REST API, and a Discord bot. A voice-controlled AI assistant that takes spoken questions, analyzes uploaded images, and replies out loud, useful for hands-free situations like a clinician dictating notes or reviewing an image without touching a keyboard. It supports OpenAI, Anthropic, and local models. Tools I use most often: LangChain and LangGraph for RAG systems, OpenAI and Hugging Face for AI models, Pinecone and ChromaDB for document search, and Docker and AWS for hosting. For the application side, I use React, Node.js, and PostgreSQL to build dashboards and connect everything together. Healthcare data needs careful handling. On my RAG project, I built automatic detection and removal of protected health information before any data entered the system, so documents could be searched without exposing patient details. On every project, I also set up role-based access and encrypt data both in storage and in transit. If you have a healthcare AI project in mind, send me a message with the type of data you're working with and the main question you want your system to answer. I will reply within a day with some initial thoughts.

Most data pipelines don't fail loudly. They rot quietly. A job starts silently skipping rows. A dashboard still loads, but the numbers stopped matching reality weeks ago. The warehouse bill creeps up because nobody ever tuned the queries. Eventually someone exports to a spreadsheet "just to be sure" and that's the moment the system stopped being trusted. That's the work I do: build data pipelines and warehouses people actually rely on, and fix the ones that quietly stopped working. Over the last 3 years I've delivered 55+ data projects across finance, healthcare, energy, and e-commerce, from one-off ETL jobs to platforms processing billions of records a day. I work GCP-first (BigQuery, Airflow, dbt, Dataflow), and I'm comfortable across AWS, Postgres, and the messy real-world stack most teams actually have. What I build: - End-to-end ETL/ELT pipelines in Python, SQL, Airflow, and dbt - BigQuery / Snowflake / Redshift warehouses and data models that stay clean as they grow - Migrations off legacy jobs and on-prem databases — without losing data in the move - Metabase, Looker Studio, and Power BI dashboards your team will actually open - Query and cost optimization when your warehouse bill stops making sense - API integrations with proper logging, retries, and checkpoints, so failures are visible instead of silent You probably need me if: - Your pipelines break and you hear it from a stakeholder, not an alert - Reports run slow, cost too much, or quietly disagree with each other - A previous developer left and nobody fully understands the setup anymore - You're scaling fast and the current data stack is starting to crack A few real results: - Architected pipelines processing 5B+ records daily at 99% reliability - Cut a client's warehouse costs ~50% by migrating legacy jobs to BigQuery - 4× throughput and 70% faster ingestion on an API pipeline pulling 2K+ domains a day - 40% faster pipeline runs through Airflow optimization How I work: a clear yes/no on feasibility before you commit, regular updates, and no disappearing mid-project. Most clients come back — usually because fixing one thing surfaces the next. If that sounds like your situation, send a short note on what's breaking or what you're trying to build, and I'll tell you straight what it'll take.

I have a decade of hands-on experience in machine learning and Python backend development, and I have a strong informatics background (Ph.D. in Bioinformatics ). In addition to my Ph.D., I obtained several certificates from world-renowned institutions in data science, which helped me form sound hypotheses and clear conclusions combined with concise visualizations. I can help you build the whole ML pipeline from scratch or improve your solutions. I can handle various tasks, such as NLP, Time Series Analysis, Computer Vision, GANs, developing bots, deploying APIs for ML models, etc. Skills: - Languages: Python & R - Frameworks: PyTorch, TensorFlow, Keras, Flask - Libraries: LangChain, NumPy, Pandas, Scikit-Learn, Plotly, SciPy, NLTK, LightGBM, XGBoost - DB: MySQL, PostgreSQL, MongoDB - Version control: GIT - Other: Docker, Google Cloud, LLM, ChatGPT, LLaMA

𝗔𝗜/𝗠𝗟 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 | 𝗦𝗶𝗴𝗻𝗮𝗹 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀 | 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 | 𝗩𝗼𝗶𝗰𝗲 𝗔𝗜 & 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗔𝗜 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿 (𝗦𝗧𝗧/𝗧𝗧𝗦 + 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗮𝗴𝗲𝗻𝘁𝘀) | 𝗪𝗲𝗯𝗥𝗧𝗖/𝗟𝗶𝘃𝗲𝗞𝗶𝘁 | 𝗦𝗜𝗣/𝗩𝗼𝗜𝗣 (𝗧𝘄𝗶𝗹𝗶𝗼/𝗔𝘀𝘁𝗲𝗿𝗶𝘀𝗸) I build 𝘃𝗼𝗶𝗰𝗲 𝗮𝗴𝗲𝗻𝘁𝘀 and 𝗹𝗼𝘄-𝗹𝗮𝘁𝗲𝗻𝗰𝘆 𝗮𝘂𝗱𝗶𝗼/𝘃𝗶𝗱𝗲𝗼 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 that actually hold up on real calls, in production, under load. If you're fighting latency, 𝗰𝗮𝗹𝗹 𝗱𝗿𝗼𝗽𝘀, 𝗡𝗔𝗧/𝗧𝗨𝗥𝗡 𝗶𝘀𝘀𝘂𝗲𝘀, 𝗿𝗼𝗯𝗼𝘁𝗶𝗰 𝘁𝘂𝗿𝗻-𝘁𝗮𝗸𝗶𝗻𝗴, 𝗿𝗲𝗰𝗼𝗿𝗱𝗶𝗻𝗴 𝗳𝗮𝗶𝗹𝘂𝗿𝗲𝘀 or a 𝘃𝗼𝗶𝗰𝗲 𝗮𝗴𝗲𝗻𝘁 that breaks the moment a real customer interrupts it, I can take it end-to-end and ship something stable. I focus on what client's measure, response latency, barge-in handling, RTT, jitter, packet loss, concurrency and cost per call. 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝘀𝘁𝗿𝗲𝗻𝗴𝘁𝗵𝘀 𝗶𝗻𝗰𝗹𝘂𝗱𝗲: Python, NumPy, SciPy, Pandas, Scikit-learn ML model development + evaluation Audio/signal processing pipelines Feature engineering + algorithm optimization Distributed computing concepts Real-time data handling & automation 𝗪𝗵𝗮𝘁 𝗜 𝗯𝗿𝗶𝗻𝗴 𝘁𝗼 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝘁𝗲𝗮𝗺𝘀: Research-backed algorithm design Strong mathematical and analytical foundation Production-aware AI/ML integration System-level thinking (latency, throughput, scalability) Clear documentation and reproducible workflows 𝗖𝗼𝗿𝗲 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 (𝗦𝘂𝗺𝗺𝗮𝗿𝘆): WebRTC / LiveKit Engineering SFU scaling strategies, TURN/STUN configuration, ICE tuning Optimizing jitter, packet loss, simulcast/SVC, bitrate adaptation Debugging join failures, audio/video inconsistencies, device issues 𝗠𝗲𝗱𝗶𝗮 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀: FFmpeg and OBS workflows for ingest → process → record → distribute Reliable multi-track recording and egress configurations Low-latency transcoding and optimized CPU/GPU usage 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝘃𝗲 & 𝟯𝗗 𝗦𝘁𝗿𝗲𝗮𝗺𝗶𝗻𝗴: Unity, Unreal and Omniverse real-time streaming setups Digital twin + avatar pipelines with synchronized audio/video Ultra-low-latency input → render → stream loops 𝗔𝗜/𝗠𝗟 & 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻: Real-time OCR, text extraction, scene understanding Background removal, segmentation, enhancement Avatar/lip-sync triggers, real-time CV analytics ML inference integrated into WebRTC with strict latency control 𝗗𝗲𝘃𝗢𝗽𝘀 & 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Dockerized deployments, environment separation Monitoring (jitter, RTT, CPU/mem, packet loss, TURN usage) Alerting, runbooks, load testing, failure-mode analysis 𝗖𝗼𝗿𝗲 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀: WebRTC / LiveKit Engineering Media Pipelines Interactive & 3D Streaming AI/ML & Computer Vision DevOps & Observability 𝗠𝘆 𝗺𝗶𝘀𝘀𝗶𝗼𝗻: To create next-generation communication systems by merging AI/ML, signal processing and automation and to invent efficient algorithmic approaches that solve complex real-world challenges. Need a solid solution? Drop a text anytime

𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭 | 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠, 𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 & 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 (𝐏𝐲𝐭𝐡𝐨𝐧, 𝐑) I'm a Data Scientist with 2+ years of experience in Data Scraping, Data Analysis, Data Visualization, Machine Learning, Deep Learning, Computer Vision, and NLP. I work primarily in Python and R/RStudio. 𝐖𝐡𝐚𝐭 𝐈 𝐃𝐨: 📊 Statistical Analysis, Time Series Analysis, Quantitative & Predictive Analysis using Python and R 🤖 Designing, developing, and fine-tuning ML/DL models that deliver real impact 🚀 Building models from scratch or optimizing existing ones in both Python and R Background: Currently pursuing a Master's in Data Science with hands-on experience across Machine Learning, Deep Learning, and Advanced Analytics using Python and R. 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐒𝐤𝐢𝐥𝐥𝐬: Languages: Python 🐍, R 📈 Frameworks: TensorFlow, Keras, PyTorch, Scikit-learn, Hugging Face Transformers, XGBoost, LightGBM, CatBoost, OpenCV, FastAPI, Flask, Streamlit Libraries: NumPy, Pandas, Matplotlib, Seaborn, Plotly, Statsmodels, SciPy (Python) and tidyverse, ggplot2, caret (R) Data Tools: Selenium, Scrapy, SQL, Power BI, Tableau, AWS (S3, Glue, Redshift), GCP, Azure ML Others: MATLAB, Git, Docker, Google Earth Engine Project Experience: I've delivered projects across healthcare, finance, computer vision, and geospatial analytics using both Python and R. Some highlights: Built deep learning models for drought prediction using 20 years of satellite data Developed medical image classification systems for cervical cancer detection using DenseNet, ResNet, and EfficientNet architectures Designed sales forecasting pipelines and stock market analysis dashboards with interactive visualizations Ran statistical analyses and regression models in R for research-focused projects Created an AI voice assistant for paramedics Built a real-time multi-camera object tracking system Developed a student pressure dashboard powered by D3.js Explored XAI methods (GradCam, SHAP, LIME) for model interpretability Implemented models for churn prediction and match outcome forecasting These projects combine machine learning, deep learning, and data science (in Python and R) to deliver practical, impactful solutions. 𝐖𝐡𝐚𝐭 𝐈 𝐎𝐟𝐟𝐞𝐫: ✅ Machine Learning Models: Build, fine-tune & deploy high-performance ML solutions ✅ Deep Learning Architectures: Custom DL models tailored to your needs ✅ Python & R Programming: Clean, optimized code for ML & data science tasks ✅ Data Analytics & Visualization: Insightful analysis and compelling visuals ✅ Data Scraping & Parsing: Data extraction using BeautifulSoup, Scrapy, and Selenium 𝐖𝐡𝐲 𝐖𝐨𝐫𝐤 𝐖𝐢𝐭𝐡 𝐌𝐞: ✨ Strong expertise in both ML and DL ✨ Clear communication throughout projects ✨ Fast delivery with professional support ✨ Focus on quality and client satisfaction I take pride in delivering solutions that work. My approach is straightforward: build powerful models, provide actionable insights, and ensure clients are happy with the results. Let's Work Together: If you need someone who can take your machine learning projects to the next level in Python or R I'm here to help turn your ideas into reality. 🚀