Hire the Best Scikit-Learn Specialists

Hi, I`m Noor 👋 "I turn raw data into clear, actionable insights that empower better decisions and drive real results, every step of the way." I'm a Data Scientist and AI Engineer. Over the past 5+ years, I’ve worked on projects that combine data engineering, machine learning, and large language models to build intelligent, production-ready solutions. I specialize in designing end-to-end ML pipelines, developing LLM-powered applications (RAG, LangChain, Llama-2/3, OpenAI), and deploying scalable systems on Azure, Databricks, and Docker. My work often involves automating data workflows, improving prediction accuracy, and transforming complex data into clear insights. Some of my favorite projects include building an AI chatbot for e-commerce, a predictive system for event planning, and an IoT protocol translator using LLMs. I value clarity, efficiency, and collaboration and I always aim to deliver results that make a measurable impact. If you’re looking for someone who can turn your data or AI idea into a working solution, I’d be happy to help.

Greetings! I specialize in AI & Machine Learning, Data Science, and MLOps, crafting scalable solutions that turn data into powerful insights and real-world impact. Skills: 🎯 Highly skilled in Python, with extensive experience in machine learning, deep learning, and AI. 🎯 Proficient in data extraction using Scrapy and BeautifulSoup. 🎯 Experienced in working with NLP tasks using spaCy, NLTK, and Hugging Face. 🎯 Skilled in Generative AI and using models like Transformers, DALL-E, CLIP, Stable Diffusion, and Vision Transformers (ViT). 🎯 Advanced in machine learning algorithms, including XGBoost, LightGBM, Scikit-Learn, and TensorFlow. 🎯 Highly skilled in working with time series data and models like LSTM, Pandas, and XGBoost. 🎯 Proficient in Data Science and Data Visualization, with hands-on experience in Pandas, Numpy, Matplotlib, Seaborn, Plotly, and Dask. 🎯 Experienced in using MLOps tools like MLflow, DVC, TensorBoard, and BentoML for model management and deployment. 🎯 Proficient in SQL & NoSQL databases, including PostgreSQL, MySQL, and MongoDB. 🎯 Skilled in cloud computing and deployment, including AWS, Docker, Kubernetes, and SageMaker. 🎯 Skilled in CI/CD practices, version control using Git and GitHub. 🎯 Comfortable working on both Linux (Ubuntu) and Windows Operating Systems. Data Exploration & Visualization: 🎯 Streamlit for creating dynamic data applications. 🎯 Power BI and Tableau for building interactive and professional dashboards. Tools: 🎯 VSCode, Jupyter Notebook, GitHub, Trello, Microsoft To-Do, Teams, Zoom, and Slack for efficient collaboration and project management.

I build production-grade GenAI systems for Fortune 500 enterprises including Verizon and PNC, : AI agents, RAG platforms, semantic layers, knowledge graphs, text-to-SQL/NLQ systems, and full-stack AI applications. I have 9+ years of experience across machine learning, computer vision, NLP, backend engineering, and full-stack product development. Recently, I have led large engineering teams building enterprise AI platforms for telecom and banking use cases. What I can help you build: • AI agent systems using LangGraph, LangChain, MCP, A2A, FastAPI, and custom tool orchestration • RAG applications over documents, databases, knowledge bases, and enterprise data • Semantic layers and ontology-driven systems for analytics and GenAI use cases • Text-to-SQL / natural language database querying over Postgres, BigQuery, and Teradata • Knowledge graph RAG using Neo4j, vector databases, metadata extraction, and domain modeling • Full-stack AI SaaS products with Python, React, Next.js, Docker, Kubernetes, and cloud deployment • LLM fine-tuning, evaluation, prompt engineering, retrieval evaluation, and hallucination reduction Relevant achievements: • Led a 14-member team to build an asynchronous, extensible, A2A-compliant agentic framework for enterprise troubleshooting. • Reduced ticket resolution time in a production troubleshooting workflow from days to minutes. • Built and deployed hundreds of tools across multiple MCP servers. • Led a 12-member team to build a natural language query platform for enterprise databases. • Improved NLQ accuracy using domain modeling, metadata generation, and knowledge graph traversal. • Built an ontology-powered Knowledge Graph RAG platform over unstructured enterprise data. • Co-founded and scaled an AI video translation/dubbing product from $0 to $100k revenue in 6 months. • Built computer vision systems deployed across industrial locations in India and Europe. Tech stack: Python, FastAPI, LangChain, LangGraph, LlamaIndex, OpenAI, HuggingFace, PyTorch, PEFT/LoRA, React, Next.js, Postgres, BigQuery, Neo4j, Pinecone, Docker, Kubernetes, ArgoCD, GCP, AWS, PySpark, Airflow. I am best suited for clients who need more than a prototype. I can help you go from ambiguous AI idea → system design → implementation → deployment → evaluation → production hardening.

Top Rated Plus | Top 10 Machine Learning Agency on Upwork | $500K+ Earned | 8+ Years in AI & Software Engineering I'm an AI Engineer building production-grade AI agent and RAG systems, not simple prompt wrappers. With 8+ years in software engineering and AI development and $500K+ earned on Upwork, I hold Top Rated Plus status and our team is ranked among the Top 10 Machine Learning agencies on the platform. I work with companies that need an AI system to actually run in production, handle real user traffic, and stay accurate, not a demo that breaks on the first edge case. As an AI Engineer, my core work covers retrieval-augmented generation pipelines, agentic workflows with tool calling and structured outputs, prompt engineering, and LLM integration with OpenAI, Anthropic, and Gemini APIs. This is the kind of system clients need an AI Engineer for: not a chatbot that answers FAQs, but an agent that retrieves the right information, calls the right tools, and follows through on what the user actually needs, with hallucination reduction and evaluation built in from the start. As a Machine Learning Engineer and Data Scientist, I build systems for structured and time-series data: demand forecasting, anomaly detection, biomedical signal analysis, and structural health monitoring. My data scientist workflow covers Python, scikit-learn, pandas, NumPy, and SciPy alongside deep learning frameworks including TensorFlow, PyTorch, and Keras, with experiment tracking and evaluation metrics to ensure models perform consistently in production. When a project needs predictive or classification models alongside the AI agent itself, I own that layer too as a Machine Learning Engineer. As a Computer Vision Engineer and software engineer, I build object detection, multi-object tracking, pose estimation, and image segmentation systems using OpenCV, YOLO, and deep learning architectures. Where this intersects with the AI Engineer work is in multimodal systems and computer vision agents: I work with Vision Language Models (VLMs) to build AI pipelines that understand images and video, not just text. Most AI Engineers only work with text. I bring production computer vision and deep learning experience on top of the LLM layer, which matters for any product where the AI needs to see, not just read. On the engineering side, I work as a Python developer and software engineer building backend services with FastAPI, vector databases including Pinecone and pgvector, and LangChain or custom orchestration for multi-step agent logic. When a project needs full ownership of both the AI layer and the surrounding application, I work as a Full Stack AI Developer, handling backend APIs, database design, and frontend integration so the AI system ships as a complete product, not just a backend script. I work with a specialized team that includes a computer vision PhD, deep learning researchers, and mathematical optimization specialists. This lets me scope larger systems, split orchestration, retrieval, and evaluation work across the team, and deliver a full AI Engineer and Machine Learning Engineer engagement faster than a solo contributor could, with the software engineering discipline of clean APIs, logging, and testing baked in from day one. Clients typically work with me when they need: - an AI Engineer to build a RAG pipeline, AI agent, or chatbot that actually works in production - a Machine Learning Engineer or Data Scientist to build predictive models or structured data pipelines - a Computer Vision Engineer to add visual understanding or VLM-based reasoning to an AI product - a Software Engineer who understands LLM orchestration, tool calling, vector search, and backend architecture - a Python developer who can own the full stack from model training to deployed API If you need an AI Engineer and a software engineer with the full stack from prompt design to production deployment, let's talk. Main stack: Python, OpenAI API, Anthropic Claude, Gemini, LangChain, LangGraph, LlamaIndex, FastAPI, Pinecone, pgvector, TensorFlow, PyTorch, Keras, OpenCV, YOLO, Docker, PostgreSQL, JavaScript, Git.

✨ E-learning Developer & Creative Strategist | AI-Enhanced Learning Solutions With a commitment to delivering results and over 5 years of experience, I specialize in developing e-learning content that’s not only educational, visually compelling, and deeply engaging. I am a self-driven problem solver, equipped to transform ideas into impactful learning experiences. 🌟 Key Strengths: 🎯 Goal-oriented and proactive mindset. 🤝 Skilled in collaboration, with a natural ability to take the lead on projects. 📚 Expertise in crafting engaging, interactive learning modules using AI and cutting-edge tools. 📈 Passionate about the intersection of e-learning, marketing, and instructional design, I blend technical skills with strategic insights to create resonant content. With AI tools at my disposal, I bring advanced interactivity, personalization, and engagement to every project. If you’re looking for a partner who can elevate your online courses with innovative solutions, 📩 let’s connect and make your vision a reality!

Most AI projects don't fail because of bad code — they fail because nobody checked if the results can actually be trusted. I build and validate intelligent systems: agentic pipelines, LLM-powered workflows, and the evaluation frameworks that make them safe to act on. My work sits at the intersection most engineers avoid: where technical results meet real-world accountability. Recently, my focus has expanded into agentic AI engineering and AI security — designing multi-agent systems with built-in quality loops, hallucination detection, bias auditing, and compliance filters. I don't just build pipelines; I build pipelines that check themselves. I've worked across computer vision, NLP, OCR pipelines, and clinical/academic research — including cell classification, ASR data, and publication-ready statistical analysis. Where I add the most value: — You need an agentic system that produces outputs you can actually trust and act on — You have a model or dataset and need to know if it's reliable before it goes anywhere near a decision — You're writing a thesis, paper, or clinical report and need analysis that survives peer review — Your stakeholders need to understand what the AI actually found — and what it didn't — You need an honest assessment of where your AI system could fail, be gamed, or cause harm I'll tell you honestly if ML isn't the right solution for your problem. That's rarer than it sounds. Core skills: Agentic AI Engineering · LLM Systems & Evaluation · AI Security & Governance · Hallucination & Bias Detection · ML Validation & Evaluation · Healthcare & Academic Data Analysis · NLP · Computer Vision · Statistical Analysis · AI Risk Assessment · Research Reporting Tools & Technologies Agentic & LLM Systems: LangGraph · LangChain · Groq · OpenAI · Pydantic · FastAPI AI Security & Governance: Hallucination detection · Bias auditing · Compliance filtering · Adversarial input testing Validation & Evaluation: MLflow · Weights & Biases · Arize AI · SHAP · LIME · Fairlearn · Great Expectations ML & Deep Learning: Python · PyTorch · TensorFlow · Scikit-learn · Keras Data & Statistics: R · IBM SPSS · Pandas · NumPy · SciPy · Statsmodels NLP & Computer Vision: Hugging Face Transformers · NLTK · OpenCV Data Quality & Pipelines: Great Expectations · DVC · Pandas Profiling Visualization & Reporting: Power BI · Matplotlib · Seaborn · Plotly Cloud & Infrastructure: AWS · GCP · Google BigQuery · Azure