AI Agent & LLM Engineer | RAG, LangChain, LangGraph, Multi-Agent Systems I build production AI agents, RAG systems, and full-stack AI apps that hold up outside a demo. Most of my work is LangChain/LangGraph agents, retrieval pipelines that don't hallucinate, and shipping the whole thing on AWS with a real frontend attached. 100% Job Success, 5.0 rating, and I'm available now. What I actually build: Autonomous and multi-agent systems with LangGraph and CrewAI — tool use, function calling, and workflows that run unattended in production, not just in a notebook. RAG and chatbots on Pinecone, Chroma, Weaviate, or Qdrant. I do hybrid search, reranking, and guardrails because retrieval quality is usually where these projects live or die. LLM engineering: fine-tuning open models (Llama, Mistral, Mixtral) with LoRA/QLoRA, plus integrations with OpenAI, Claude, and Gemini. I care about evals and cost, not just getting a response back. Full-stack AI SaaS end to end — FastAPI or Django backend, Next.js/React frontend, Postgres or Mongo, Stripe, auth, deployed on AWS or Vercel. I also do AI automation (n8n, Make, custom Python), computer vision with YOLOv8, and real-time voice (speech-to-text and speech-to-speech) when a project needs it. Recent example: [e.g. "Built a RAG support assistant that cut a client's average response time from 6 minutes to under 40 seconds across ~2,000 tickets/month."] — replace with one real result; this single line does more than any tool list. Stack: Python, LangChain, LangGraph, CrewAI, PyTorch, TensorFlow, YOLOv8 · FastAPI, Django, Flask, Node.js · Next.js, React, TypeScript, Tailwind · Pinecone, Chroma, Weaviate, Qdrant, Postgres, MongoDB, Redis · AWS, GCP, Docker, Vercel, n8n. Why clients keep me around: I ship fast, I tell you what's realistic before you've paid, I test before I hand anything over, and I don't vanish after the invoice clears. If you're building an AI product, automating a workflow, or need a full-stack system that works in the real world, send me the brief. I'll tell you honestly what's possible and how long it'll take.

🔷️ Looking for a Fast learner, Problem Solver ? New project/use cases are most Welcome. Its all about Research and figuring things out. And I love to do that (R&D). Always a Problem Solver. 🔷️I am an Expert in writing Python Scripts, Machine Learning, Data Science, SQL, Snowflake, Streamlit, LLM's, Arduino projects, Automation tasks, Data extraction from PDFs. 🔸️ Ecommerce, Finance, EdTech, Medical, Sensors, Agriculture, Machines etc. Usecase Industries. 🔸️ Professional Data Science work on unstructured/structured datasets. 🔸️ Data Analysis on Sales/transaction data. 🔸️ Purchase Pattern prediction, Customer behaviour analysis, Product Recommendation Systems. 🔸️ Full-fledged Streamlit Dashboard/webapp for Interactive Data visualization, and analysis. 🔸️Fine tuning LLM's, RAG pipeline, Get your Chatbot/ GPT/ LLM trained on your custom dataset. 🔸️ Image Annotation, Evaluating AI responses/AI training ✔ Pandas, NumPy — data cleaning, missing value handling, outlier treatment, data analysis. ✔ Scikit-learn — ML pipelines, feature engineering, hyperparameter tuning, model optimization. ✔ NLP - Text preprocessing, tokenization, embeddings, tf-idf, Word2vev, SBERT, similarity models. ✔ ML Models — LR, SVM, KNN, Decision Tree, Random Forest, XGBoost, PCA, SMOTE etc. ✔ Visualization & Environment — Matplotlib, Seaborn, Jupyter Notebook, Google Colab. 🔸️Things I love to do : ✔ Writing Python Scripts ✔ Python scripts/code debugging ✔ Machine Learning / Data Science / Python projects ✔ Automation with Python ✔ Basic to Advanced Arduino Projects (PID Algorithm, sensors, motors, pneumatics and many more) 🔸️Also having experience of more than 2 years of Arduino Programming on lot of sensors, modules, motors, motor drivers like Cytron, L298N, LSA08, Ultrasonic, IR sensor, Actuators, Pneumatics, Line Follower, Wall Follower, and high-level autonomous and manual omni-wheel drive robots ⭐️ I always try to give more than expected, with better quality. ⭐️ Finishes a lot before the deadline. ⭐️ Always ready to sort out the issues, and to help in every possible way. ⭐️ Clients satisfaction is my priority. Never wants to disappoint my clients. ⭐️ Ready to help. 🙂 Ping me to discuss the project details. Thanks a lot. 🙂

AI Engineer with over 6 years of hands-on experience in Natural Language Processing (NLP), Computer Vision, and Recommender Systems. With a proven track record on Kaggle, I'm ranked in the top 10% of competitors worldwide. 🌍 My expertise extends to designing, training, testing, and deploying end-to-end applications using Django, Nginx, and Gunicorn on platforms such as AWS, Heroku, and GCP. I have a solid background in working with REST APIs and containers, ensuring smooth integration and deployment of AI solutions. 🚀 My technical toolkit includes: 🔸 Deep Learning Models: Keras, TensorFlow, PyTorch 🔸 Batch & Stream Processing: Spark SQL 🔸 Backend Development: PostgreSQL 🔸 AWS Services: S3, Kinesis, Dynamo DB, Elasticsearch, Redis via Boto3 In the NLP domain, I have worked on developing chatbots, data extraction pipelines, text classification, and sequence labeling. I have also finetuned large language models like GPT-3 and LLAMA for specific use cases. In Computer Vision, I've worked on object detection, OCR models, player and ball tracking in sports analytics, video analytics and more. I have a track record of creating efficient models for fast processing and small memory usage. In Generative AI, I have developed high-quality image generation models using stable diffusion and text generation models using OpenAI GPT models. I've had the privilege to work with diverse remote teams from the USA, Russia, Germany, Japan, and the Philippines, honing my collaboration and communication skills. Let's leverage AI to transform your business processes, enhance customer experiences, and unlock new opportunities. Reach out to discuss how we can collaborate to achieve your goals. Looking forward to helping you bring your AI vision to life! 😊

Expert-Vetted Top 1% on Upwork | Top 10 Machine Learning Agency on Upwork | 7+ Years in Production AI | Sports, Industrial, Satellite, Healthcare I'm a Computer Vision Engineer and Machine Learning Engineer with 7+ years delivering production-grade AI systems. Upwork has Expert-Vetted me as a Top 1% specialist in this niche, and our team is ranked among the Top 10 Machine Learning agencies on Upwork. I work with product teams and startups across sports analytics, industrial inspection, satellite and aerial imagery, access control, healthcare, and generative AI — any domain where visual data needs to become reliable, actionable output running in production. As a Computer Vision Engineer, my core work covers object detection, multi-object tracking, pose estimation, image segmentation, image processing, and real-time video analysis. I build end-to-end pipelines in Python using OpenCV, PyTorch, TensorFlow, and Keras, from dataset preparation and model training through TensorRT optimization and Docker deployment on cloud or NVIDIA Jetson edge hardware. I use C++ for performance-critical components where Python latency is a bottleneck. The domains where computer vision engineer experience creates the most value: sports analytics (player tracking, performance metrics, automated statistics from broadcast video), industrial inspection (defect detection and quality control on production lines), satellite and aerial imagery (object detection and segmentation for infrastructure analysis), access control and security (vehicle identification, multi-camera real-time monitoring), and healthcare and biomechanics (pose analysis, body measurement, and biomedical signal processing connected to AI coaching backends). As a Machine Learning Engineer and Data Scientist, I also build systems for structured and time-series data: demand forecasting, anomaly detection, biomedical signal analysis, and structural health monitoring. My data scientist workflow covers scikit-learn, pandas, NumPy, and SciPy alongside deep learning frameworks, with experiment tracking and evaluation metrics to ensure models perform consistently in production. When projects require generative AI or LLM components, I deliver RAG pipelines with LangChain and vector databases, synthetic dataset generation tools, and document processing systems using the Gemini API. Regardless of domain, the computer vision engineer approach stays the same: combine OpenCV-based preprocessing with deep learning inference into a scalable, testable pipeline that holds up under real-world conditions — variable lighting, occlusion, low resolution, multi-camera setups, and edge hardware constraints. I work with YOLO-family models, ByteTrack and DeepSORT for tracking, MediaPipe and MMPose for pose estimation, TensorRT and ONNX for inference optimization, and FastAPI with Docker for production deployment. I work with a specialized team that includes a computer vision PhD, deep learning researchers, and mathematical optimization specialists. This lets me scope complex systems, split parallel workstreams, and deliver a full Computer Vision Engineer engagement faster than a solo contributor could. Clients typically work with me when they need: - a Computer Vision Engineer to build detection, tracking, or segmentation systems from scratch - a Machine Learning Engineer to productionize a research model and meet latency requirements - a Data Scientist who can go from raw data to a deployable model end-to-end - an AI engineer to integrate LLM or generative components into an existing backend - real-time or edge inference optimized for NVIDIA Jetson or mobile deployment - a Python developer who understands both the AI pipeline and the surrounding system architecture If you need a Computer Vision Engineer with the full stack from dataset to deployed API, let's talk. Main stack: Python, OpenCV, PyTorch, TensorFlow, Keras, YOLO, ByteTrack, MediaPipe, MMPose, CoreML, TFLite, TensorRT, ONNX, scikit-learn, FastAPI, Docker, PostgreSQL, C++, NumPy, pandas, SciPy.

I build AI solutions for Computer Vision and NLP. My experience covers object detection (YOLO), image segmentation, classification, and text-based applications such as RAG pipelines and large language model fine-tuning. I focus on building models that are both accurate and efficient, with practical experience in preprocessing, model training, evaluation (precision/recall, mAP, F1), and deployment using Python, PyTorch, TensorFlow, Hugging Face, and OpenCV. Whether you need to process images, analyze text, or integrate AI into your workflow, I can help you deliver a reliable solution tailored to your data.

💡 NLP, Machine Learning, and Deep Learning Implementations 💡 Model Deployments, Data analysis and Visualizations 💡 Custom & Interactive Dashboards and ML Apps - Expertise with: NLP, ML, BigData tools GPT3, T5, custom transformer implementations Large Language Models (LLM's) ChatGPT, GPT index, Langchain Prompt Engineering Language Modelling Topic Modelling, Text Analytics Word Embeddings, Transformer Embeddings Text Similarity, Similarity Engines Recommendation Engines, Ranking Engines Classification Models, Predictive Models Time Series Analysis Classical ML (Regression Analysis, Clustering, Random Forests, Statistical Analysis, Gradient Boosting etc) OCR and Document Parsing (Extracting Tables, Text, etc) Pytorch, Pyspark, Scikit-Learn Python: Django Rest Framework, Flask, Cronjob, Celery, RabbitMQ, Web scraping, Crawling, Data Mining, Micro-Services, Logging & Debugging Database Integrations: Elasticsearch, ArangoDB, SQL, PSQL, SQLLite, MongoDB, CassandraDB, Custom integrations Deployments: Gunicorn, Nginx, Docker, Docker-Compose, AWS, Azure, GCP - About me I am Hamza Shaheen and I am a data scientist with half a decade of experience in machine learning. I will help you with your Python Problems and projects. My services include Data Cleaning, Processing, Analysis, and visualization using Python. I have all of the experience and skills you are looking for and I will be the perfect person for your business. I have a history of always completing my projects ahead of schedule. I try my best to exceed clients' expectations with high-quality work. I would love to assist you in your business in various ways and I am looking forward to it.

🥇🎖️Upwork Expert-Vetted Data Scientist and Generative AI Engineer (Top 1%) |🎖️ Upwork Top Rated Plus Freelancer |🧑‍💻 7+ years of professional experience in Data Science and Machine Learning | 🎯 Completed long term projects for Capgemini and EveryRate and consulted for companies including JKI, CheckCare, and a handful of startups | AWS Community Builder in Machine Learning and GenAI In my 7+ years of career, I have worn many hats, including Data Scientist, Machine Learning Engineer, and Generative AI Engineer. My recent experiences: 1. Content recommendation: I have trained and deployed recommender systems to personalize users' experience mostly with Amazon Personalize and Vertex AI Applications (google discovery engine). 2. Experimentation: Worked on Causal Inference (RDD and Propensity Score Matching), A/B testing, Interleaving Recommendations Experiments, and Multi-armed Bandit experiments. 3. Built Generative AI applications, including RAG-based chatbots, Text2SQL, and Text-to-Speech applications. Fine-tuned and deployed open-source LLMs. 4. Document Processing with OCR, ETL, and RPA. 5. Time Series Analysis and Forecasting. Projects succeed when proper timelines, realistic scopes, and appropriate budgets are set early. Thus, I make it a point to understand the project thoroughly and ensure my client's expectations align with the timeline and budget. The time invested in this planning stage has always paid off. Let's connect today! Interested in a quick 30-minute or 1-hour consultation to discuss your project before starting a contract? Click on "Book a Consultation" below. During the session, I will share more detailed information about your intended project. We will also discuss the feasibility, cost, and tentative timeline. Relevant documents will also be shared after the meeting. DETAILED EXPERIENCES and SKILLS: Data Science | Machine Learning | Deep Learning Expertise: Text extraction and Processing (OCR): Google Document AI, Azure AI Document Intelligence, Amazon Textract. Data Engineering: Databricks, Apache Spark, SQL databases Regression - Univariate and Multivariate Time series analysis and forecasting: ARIMA, SARIMAX, VAR, VECM, LSTM, and Facebook Prophet. Classification: Logistic Regression, Decision Trees, Random Forest, Xgboost, Naive Bayes, SVM, Shallow, and Deep Neural Networks, and Ensemble of Models. Unsupervised Learning: K-means clustering (Scikit and FAISS), Topic Modeling (including NMF Frobenius norm and KL divergence, LDA, and LSA), Dimensionality reduction (including MDS, UMAP, PCA, t-SNE, LSTA, LDA, and Hessian, and Truncated SVD) Tools and Frameworks: Python (Numpy, Pandas, Scikit-Learn, Keras, PyTorch, Tensorflow, and BeautifulSoup) Data Visualization: D3.js, Python (Matplotlib, Seaborn, Plotly), and Tableau Platforms: Amazon Sagemaker, Google Cloud (Vertex AI), Azure ML Tools: Docker, Amazon Glue. Generative AI Expertise: I have built and integrated conversation AI applications into existing applications for businesses. Some of the specific cases include: 1. A generative AI application for a beauty brand that generates product images, captivating product descriptions, and beauty models before and after transformation on the landing page of the client's website. The generated content is automatically tailored to the customer's profile, especially demographics and purchase history. ⚙️Tools and services used: Google Cloud Platform, Vertex AI, Stable diffusion, PaLM-2 (text-bison) 2. An intelligent shopping assistant for a famous retail brand. The assistant leverages a combination of agents to achieve various tasks, including fetching inventory information from the knowledge graph (Neo4j on a Neptune Instance) based on a user query, responding to FAQs by leveraging context from the Kendra database, simultaneously extracting possible lead entities from the conversation and enriching the knowledge graph, driving the conversation towards making a sale. ⚙️Tools and services: Amazon Bedrock, Amazon Kendra, Amazon Neptune, AWS EC2, and Sagemaker. Others include a content generator bot for Atlassian confluence, LLM powered telegram bot, etc. NLP and GENERATIVE AI (Image Text, and Multimodal): Prompt Engineering for LLMs including both open and closed source: Mixtral, Open AI GPTs, Google PaLM-2, LLaMa, Claude, Amazon Titan, Claude, Stable diffusion, DALLE-3, and Imagen. Text Preprocessing and Sentiment Analysis, Intent Classification, Document Classification: NLP toolkit, BERT Topic Modeling and Text Classification Named Entity Recognition, Entity Extraction: Python Pydantic, Open AI function calling, and SpaCy, etc. Speech-to-Text, Text-to-Speech, and Machine Translation, Tools, Frameworks, and Techniques: LangChain, HuggingFace, Chain of thought (COT) Reasoning, and Retrieval Augmented Generation (RAG). Store and Knowledge base: 🛢️Vector Databases: Amazon Kendra, Chroma, and Pinecone; Knowledge graph: Amazon Neptune (Neo4j). Others:AWS+GCP+Azure

Hi, I’m Aniket - a Machine Learning Engineer and Quantitative Systems Developer focused on building data driven trading and analytics systems. I work with Python, Pytorch, sciki learn, Pandas, and NumPy to develop ML models, backtesting engines, and paper trading platforms for financial and market data. My work emphasizes robust evaluation, risk aware design, and clean engineering, not just model accuracy. I specialize in: - ML for Financial & Market Data - Quantitative Strategy Research & Signal Modeling - ML Based Backtesting & Paper Trading Systems - Financial Time-Series Analysis & Feature Engineering - Risk Management & Performance Evaluation - End-to-End ML & Trading Pipelines I’m also an active open source contributor to production grade Python and data science/ML libraries, which helps me deliver maintainable, scalable, and reliable systems. Whether you need strategy research, an ML backtester, or a trading analytics pipeline, I bring a practical, engineering-first mindset focused on real-world results. Let’s collaborate to transform your data into actionable outcomes, on time and with precision.

Most AI/ML projects work in notebooks — but fail in production. I build the ones that actually run. I’ve led production AI systems in real-world environments, including large-scale energy solutions for ENGIE Brasil. I design and deploy end-to-end AI systems that handle incomplete data, noisy signals, and real-world constraints. I’m a Data Scientist and AI Engineer with a Ph.D. in Cognitive Science and 12+ years of experience. On Upwork, I’ve delivered 25+ projects, $50K+ in earnings, and maintain a 100% job success score as a Top Rated Plus freelancer. My work goes beyond modeling — I build systems that integrate into real workflows. I’ve built: * LLM-based systems (RAG pipelines, AI assistants, internal copilots) * Automation agents and decision systems * Time series models for energy, anomaly detection, and predictive maintenance I work best with clients who want to build something real — not just experiments. If you already have data and a clear problem, I can help you turn it into a working system.

Namaste 🙏! I'm a passionate Data Scientist with an academic foundation from Northwestern University, deeply committed to advancing AI and Machine Learning across healthcare, computational biology, and scientific discovery—with particular strength in mathematically rigorous, FDA-aligned model development. Over the past year, I have successfully designed and implemented AI-driven solutions for ECG classification, denoising, medical imaging, and computational chemistry. My expertise blends deep neural architectures with advanced mathematics—Physics-Informed Neural Networks (PINNs), probability theory, stochastic processes, variational methods, and numerical optimization—to solve complex real-world problems with scientific defensibility. 🏥 FDA-Ready Clinical AI Development I have worked closely with FDA regulatory experts to bring models through clinical validation pipelines, ensuring they meet the rigor expected of medical-grade AI. My team is well-equipped to handle FDA documentation requirements (510(k) submissions, Software as a Medical Device (SaMD) artifacts, predicate analysis, validation reports, and risk management per IEC 62304 / ISO 14971) once a model is ready for launch. This means clients don't just get a working model—they get one built with regulatory submission in mind from day one. 🔬 Recent Work & Expertise in AI for Science & Medicine: ✅ ECG Signal Processing & Classification: Built deep learning models (CNNs, LSTMs, Transformers) for denoising, anomaly detection, and classification of ECG, PCG, PPG, and SCG signals—bringing them to gold-standard PhysioNet quality. ✅ Medical Image Processing (DICOM): Developed AI-driven solutions for lumbar spine classification, CT segmentation (TotalSegmentator models), fat tissue segmentation, bone mineral density estimation, and venous insufficiency detection via thermal imaging. ✅ Physics-Informed Neural Networks (PINNs): Applied PINNs to embed governing differential equations directly into the loss function—producing models that respect physical laws, generalize on sparse data, and are inherently more interpretable for scientific and clinical applications. ✅ Drug Discovery & Computational Biology: Used ML techniques to analyze molecular structures and optimize pharmaceutical compound discovery. ✅ Diffusion Models for Vision: Worked on Stable Diffusion and LoRA fine-tuning for text-to-video and image-to-video pipelines without relying on third-party APIs. ✅ AWS AI/ML Pipeline Deployment: Set up cloud-based pipelines on AWS for training, hosting, and deploying scalable ML models. ✅ Mathematical & Optimization Techniques: Leveraged variational calculus, FFT, wavelet transforms, probability theory, and evolutionary algorithms for feature engineering and model enhancement. 🧠 Core Technical Stack: 🚀 Deep Learning Frameworks: TensorFlow, PyTorch, JAX 📐 Mathematical Foundations: PINNs, probability and stochastic processes, variational calculus, numerical optimization, Bayesian inference 🔬 Generative & Diffusion Models: Stable Diffusion, LoRA, GANs, VAEs 📊 Classical ML: Random Forest, SVM, Gradient Boosting, RAG-based retrieval 👁 Computer Vision: CNNs, Vision Transformers, OpenCV, DICOM processing using SwinUNETR (3D U-Net + Swin), segmentation with nnU-Net 📝 NLP: Transformer-based LLMs, Hugging Face, LangChain, LlamaIndex 💡 Signal Processing: FFT, spectrograms, wavelet transforms, optimization methods ☁ Cloud & MLOps: AWS, Azure, GCP, Kubernetes, FastAPI, DevOps pipelines Why Work With Me? 🔹 Specialized in AI for healthcare, computational biology, and scientific discovery 🔹 Strong mathematical and probabilistic foundation—not just black-box deep learning 🔹 Experience working alongside FDA experts on clinical validation, with a team prepared to handle regulatory documentation 🔹 End-to-end delivery: from research and prototyping to deployment and submission-ready artifacts 🔹 Real-world projects that impact both research and industry Let's connect and build scientifically rigorous, regulation-ready AI together!