I build machine learning and artificial intelligence systems for real environments and mvp's from computer vision and object detection to automation pipelines, AI agents, RAG systems, LLM fine-tuning, predictive modeling, and e2e, data driven applications. I’ve worked across: Real-time object detection, tracking, and counting Biometric verification systems and face recognition Anomaly detection in complex systems and time series analysis Surveillance AI and crowd analysis Modeling problems where standard approaches don’t hold I handle the full stack. Research, model development, deployment, Docker, REST APIs, cloud (AWS/GCP), edge AI, and scalable systems. What i want to accomplish is building something that actually solves your problem, If you have a challenging AI or ML problem, we can figure it out together.

I am a Senior AI/ML Engineer with 6+ years of production experience building intelligent systems for clients across the USA, UK, Middle East, and Europe, 50+ projects delivered, 50+ clients served, 100% Job Success on every Upwork engagement. I operate as a hands-on AI technology partner through the full development lifecycle, from initial problem scoping and data engineering to model training, system integration, cloud deployment, and long-term maintenance. My work spans startups, funded ventures, and government institutions, including a production deployment under the United Nations Development Programme (UNDP) for the Government of Balochistan. I specialise in building AI systems that survive real-world conditions, not controlled experiments. This includes fine-tuning LLMs on custom datasets, designing multi-agent pipelines with LangGraph and CrewAI, building RAG architectures with Query Expansion and Cross-Encoder Re-ranking, and deploying computer vision systems on edge hardware. I have also engineered 10+ full-stack software products that are live and actively used by real clients today. My engineering philosophy: the last 20%, deployment, latency optimisation, and production hardening, is where most AI projects fail. That is exactly where I focus. ⦿ Industries and Domains Served Government and Public Sector, FinTech and Accounting, Real Estate and PropTech, Crypto and Web3, Career Intelligence and HR Tech, Transportation and Smart Cities, Healthcare, Research and Academia ⦿ Systems and Solutions Delivered AI Agents and Multi-Agent Systems, RAG Pipelines, LLM Fine-Tuning (SFT, LoRA, QLoRA), Custom Transformer Training from Scratch, Computer Vision Systems, NLP Pipelines, AI SaaS Platforms, Facial Recognition, Sign Language Detection, AI Voice Assistants, Crypto Intelligence Agents, Financial Forecasting, Data Dashboards, Data Engineering ⦿ Selected Work Mind of Pepe — Crypto AI Agent Fine-tuned LLaMA on real trading agent data to build a persona-driven crypto Twitter agent achieving 98% signal accuracy. Integrated a RAG system with live CoinMarketCap data and sentiment analysis into a gated meme coin trading terminal. Drove viral community engagement. Stack: LLaMA, SFT Fine-Tuning, RAG, CoinMarketCap API, Twitter API AI Receptionist — Jumper Media Full voice AI receptionist handling intelligent call routing, scheduling, follow-ups, and outbound calls autonomously. Optimised response latency to 850ms for a seamless human-like experience. Stack: n8n, Telnyx, ElevenLabs, Python Aqariiq.ai — AI Real Estate Platform (UAE) Full-stack AI platform with ML property valuation, NLP natural language search, smart recommendations, and real-time market dashboards. Built for Arabic and English markets. Stack: Machine Learning, Django, Next.js, PostgreSQL, AI Agents OnTrack Careers — AI Career Intelligence LangGraph multi-agent system scoring AI job-replacement risk, identifying skill gaps, and recommending personalised career pathways and development plans. Stack: LangGraph, OpenAI, FastAPI, Python ⦿ What Sets Me Apart 2x Kaggle Expert with published datasets and notebooks across reinforcement learning, computer vision, LLMs, NLP, and data science — recognised by the global ML community with 8 bronze medals and a dataset that reached 3rd place trending globally. I have worked on both sides — as a research assistant at the University of Balochistan CS Department training detection models that contributed to academic publication, and as a commercial engineer shipping products under tight client deadlines. That combination means I bring both depth and delivery. Unlike most AI freelancers who own one layer — modelling, or APIs, or deployment — I cover the full stack: from raw data to trained model to deployed API to monitored production system. One engineer, one contract, complete ownership. ⦿ What I Build For You AI Agents and Multi-Agent Systems, LangGraph, CrewAI, LangChain RAG Pipelines and Knowledge Systems, Chroma, Pinecone, Re-ranking LLM Fine-Tuning on Your Data, LoRA, QLoRA, SFT, LLaMA, HuggingFace Computer Vision Systems, Detection, Tracking, Segmentation, YOLO NLP Pipelines, Classification, Extraction, Translation ML Pipelines and MLOps, MLflow, DVC, Airflow, Docker, AWS Full-Stack AI APIs and SaaS, FastAPI, Django, PostgreSQL Data Science and Analytics, Forecasting, Dashboards, Power BI ⦿ Core Tech Stack AI and ML: PyTorch, TensorFlow, HuggingFace, Scikit-learn, XGBoost Generative AI: OpenAI, LLaMA, LangChain, LangGraph, CrewAI, Groq Vision: OpenCV, YOLOv8, ViT, MediaPipe, OAK-D, Jetson Nano Cloud: AWS, GCP, Azure, Docker, Kubernetes Backend: FastAPI, Django, Flask, PostgreSQL, MongoDB, Redis MLOps: MLflow, DVC, W&B, Airflow, GitHub Actions, ONNX Available 30+ hrs/week Building something with AI? Message me, let us make it production-ready.

𝐄𝐱𝐩𝐞𝐫𝐭-𝐕𝐞𝐭𝐭𝐞𝐝: 𝐓𝐨𝐩 𝟏% 𝐨𝐧 𝐔𝐩𝐰𝐨𝐫𝐤 🥇 Senior Agentic AI Developer with 6+ Years of Experience | Shipped 50+ Role-Critical AI Agent + RAG Chatbots | Conversational AI | Senior AI First Full Stack Developer with Python(FastAPI + React) | Principal AI/ML | GenAI, LLM, Fine-tuning, Cloud(Azure, AWS, GCP) | I'm Samran Elahi, a Senior AI / Machine Learning Engineer who has worked extensively with NLP and Agentic AI. I'm equally skilled in automating custom workflows, developing LLMs, building RAG Powered Chatbots, and designing Machine Learning Models along with fine-tuning and Deploying them on AWS / Azure / GCP, or Runpod. ✅ 𝐖𝐡𝐚𝐭 𝐈 𝐁𝐫𝐢𝐧𝐠 𝐭𝐨 𝐓𝐚𝐛𝐥𝐞 — 𝐀𝐈 & 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐒𝐞𝐫𝐯𝐢𝐜𝐞𝐬 𝐰𝐢𝐭𝐡 𝗙𝘂𝗹𝗹 𝗦𝘁𝗮𝗰𝗸 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗘𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲 ■ Natural Language Processing Expertise ✓ Chatbots and Conversational AI Development ■ AI Agents + RAG Chatbots Development Expertise ✓ Custom AI Agents built with LangChain / LangGraph. ✓ Role-critical MCP Agents for complex task handling. ✓ RAG-powered Chatbots with integrated LLMs (OpenAI, Claude, Mistral.) ✓ Frontend: React, Streamlit, Chainlit | Backend: FastAPI, Django. ✓ Vector DBs: Pinecone, FAISS, Weaviate. ■ Conversational AI with Real-Time Communication Capabilities ✓ Tools: ElevenLabs, Voice.ai, Retell.ai, Vapi integrations. ✓ Real-time API integration for seamless voice interactions. ■ Custom ML Model Development, Integration, and Deployment ✓ Supervised Learning: Regression, Classification. ✓ Unsupervised Learning: Clustering, Dimensionality Reduction, Anomaly Detection. ✓ Hyperparameter Tuning: GridSearch, RandomSearch, Bayesian Optimisation. ✓ Parameter-Efficient Fine-Tuning (PEFT). ✓ Model Deployment: Runpod, AWS, GCP, EC2. ✓ Model & Data Versioning: DVC. ✓ REST API Development: FastAPI, Flask, Django. 📢 Available, let's have a quick chat to go over the scope of your project. Best, Samran Elahi

I build production AI, Machine Learning, and Data Analytics systems that cut operational costs by 40%, prevent ~$2M in annual fraud losses, and turn messy enterprise data into revenue-driving Business Intelligence. Recent work includes a Machine Learning microservices platform serving 2M+ users and 100M+ daily API requests, a multilingual AI Agent handling 15,000+ daily messages with a 20-point NPS lift, and a RAG-powered AI Chatbot embedded in production web apps that drove a 42% conversion rate increase. I work end-to-end across Generative AI, LLM Engineering, AI Agent Development, AI Automation, Data Science, Data Engineering, ETL Pipelines, MLOps, and executive Dashboards in Power BI, Tableau, Looker Studio, and Google Analytics 4. The teams I work best with have data and ambition but no roadmap to ship AI in production without breaking compliance, scale, or budget. What I build: • GPT and Claude-powered AI Chatbot Development, AI Agent Development, and LLM Integration using OpenAI API, Anthropic Claude API, Google Gemini, LangChain, LlamaIndex, and RAG with Pinecone, Weaviate, and Chroma vector databases • Machine Learning and Deep Learning models for fraud detection, demand forecasting, customer segmentation, recommendation engines, and NLP • ETL Pipelines and Data Engineering on Google BigQuery, Snowflake, Databricks, AWS, Azure, and SQL • Power BI Dashboards, Looker Studio Reports, Tableau Visualizations, and full Google Analytics 4 implementation • Python Automation, Web Scraping, API Integration, and AI Workflow Automation for scalable business processes Selected results in AI, Generative AI, and Machine Learning: • GPT-powered AI Chatbot reduced support tickets 50% in one quarter • Machine Learning microservices platform serving 2M+ users and 100M+ daily API requests • Multilingual AI Agent handling 15,000+ daily messages, lifting NPS 20 points • Deep Learning fraud detection model cut false positives 40%, preventing ~$2M in annual losses • Predictive inventory AI decreased stockouts 30% and real-time customer segmentation lifted conversion 28% • NLP-based ticket triage automation improved first-response efficiency 65% • RAG-powered AI Chatbot embedded in web apps improved conversion 42% • Real-time AI Call Center Assistant cut average handle time 35% and lifted first-call resolution 22% • Predictive AI for cell tower failures reduced emergency dispatches and improved network uptime Selected results in Data Engineering and ETL: • Migrated Data Engineering stack to Google BigQuery: 60% faster queries, 25% lower storage costs • Standardized ETL Pipelines for Data Mining and Data Annotation, increasing ML training throughput • Built feature stores and reproducible MLOps training pipelines that accelerated ML iteration • Self-correcting Data Analysis pipeline reduced reporting errors 95% • Python competitive monitoring system mining 150+ sources daily into Google Sheets and BigQuery • SQL clustering and replication deployed for 99.99% uptime on mission-critical apps Selected results in Dashboards and Business Intelligence: • Looker Studio Dashboard suite reduced delivery delays 15% • Marketing Analytics Dashboards unifying paid media, web, and CRM data in Power BI and Looker Studio • C-level KPI Dashboard pack (SQL + Google Analytics 4 + Excel) accelerating weekly decisions • Server-side Google Tag Manager implementation significantly improved GA4 accuracy • Pricing optimization engines lifted profit margins 17% • Self-serve Analytics portal with NLQ-powered Power BI reduced ad-hoc requests 55% • JavaScript bridge syncing legacy Excel models with cloud databases, saving 20 hours per week Beyond implementation, I run AI Readiness Audits for executive teams that have invested in data infrastructure but not yet seen ROI, identifying high-impact AI Agent Development and Machine Learning opportunities and translating them into a 90-day rollout plan with clear success metrics and risk gates. Tech stack: Python, SQL, JavaScript, TypeScript, AWS, Google Cloud, Azure, BigQuery, Snowflake, Databricks, dbt, Airflow, Power BI, Tableau, Looker Studio, GA4, Google Tag Manager, Excel, OpenAI API, Anthropic Claude API, Google Gemini, LangChain, LlamaIndex, Hugging Face, TensorFlow, PyTorch, scikit-learn, Machine Learning, Deep Learning, Generative AI, LLM Engineering, MLOps, NLP, ETL Pipelines, Data Mining, Data Annotation, Data Visualization, AI Integration, AI Automation, AI Agent Development, AI Chatbot Development, RAG, and Vector Databases (Pinecone, Weaviate, Chroma). Share your data sources, target metrics, and timeline. I will reply within 24 hours with a practical rollout plan covering ETL Pipeline through to production AI Integration, plus a short list of risks I see before we start. I work as a strategic partner on production AI and Data systems, not a ticket-taker.

Your go-to fullstack AI engineer and workflow automation expert, I build AI systems that quietly run in production. AI Agents, n8n workflows, LLM multi-agent architecture, RAG pipelines, and the fullstack apps that wrap around them. Top Rated on Upwork. RECENT WORK: → Multilingual cancellation management system for a German B2B client, Azure OpenAI + n8n classifying vendor replies in German and English, auto-generating bilingual responses. Replaced a manual triage queue. → Mobile Application for a Saudi Arabia Client to manage workforce & work with AI integrations. → End-to-end content automation pipeline, content brief to published post, GCP OAuth, multi-stage n8n workflow. → Outbound sales engine using Apollo + Saleshandy + n8n + Claude API, enrichment, personalization, and reply handling at scale. → Multi-agent HR ops system with Web Application, specialized AI agents handling recruitment, onboarding, and policy workflows across Slack and Teams. → RAG pipelines for B2B catalogs and knowledge bases (HVAC technical specs, internal docs, support corpora) using Pinecone, pgvector, LlamaIndex, and LangChain. → Fullstack SaaS builds, multi-tenant platforms with React/Next.js frontends, FastAPI/Django backends, and AI features baked into the core product flow. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ WHERE I'M STRONGEST 1. Workflow Automation Production n8n architecture, the messy real-world kind with retries, error handling, rate limits, and observability. Not happy-path demos. Multi-system orchestration across CRMs, email, Slack, databases, and internal APIs. 2. AI Agents & LLM Systems RAG pipelines that retrieve the right thing the first time. Multi-agent orchestration with Anthropic, OpenAI, and open models. Tool use, function calling, evaluation, and production guardrails. Vector databases, embedding strategy, and retrieval tuning. 3. Fullstack Development React and Next.js on the frontend. Python (FastAPI, Django, Flask) and Node.js on the backend. PostgreSQL, MongoDB, vector DBs. REST and GraphQL APIs. End-to-end app delivery, not just the AI layer, but the dashboards, admin panels, auth, billing, and UI your users actually touch. 4. Infrastructure & Deployment Docker, CI/CD pipelines, AWS and Azure. Production monitoring, logging, and the boring reliability work that separates a demo from a system you can bill clients on.

I am an Artificial Intelligence Consultant and Machine Learning Engineer with more than 8 years of experience developing scalable, data-driven solutions for companies in technology, telecommunications, fintech, and e-commerce. My focus is on designing intelligent automation systems, conversational agents, and predictive models that improve operational efficiency and decision-making. I have collaborated with global clients such as Google, Deloitte, Globant, and Telecom, delivering high-impact AI implementations using LLMs (Gemini, GPT, LlamaIndex), LangGraph, Vertex AI, AWS SageMaker, and Google Cloud Platform. As CTO and Co-Founder at Nexia Capital, I lead the development of custom AI agents and microservices for businesses seeking to integrate automation with intelligence — bridging the gap between advanced models and real business results. Core competencies: AI Chatbots and Conversational Systems (Dialogflow CX, LangGraph, LlamaIndex) Process Automation & Cloud Microservices (AWS, GCP, Vertex AI, CI/CD) Predictive Analytics and Data Engineering (Python, SQL, Pandas, scikit-learn) Computer Vision & NLP (YOLOv8, LLMs, Prompt Engineering) Machine Learning Architecture & Deployment (Docker, API, MLOps) I’m passionate about transforming complex workflows into intelligent, automated systems that drive measurable ROI.

I build production-grade AI agents, RAG systems, and computer vision pipelines that ship on real infrastructure — not just demos. IEEE-published AI engineer (DAELocNet, AIIT 2025), Top Rated with 100% Job Success Score. If you need an LLM app that actually works in production — grounded answers, no hallucinations, controlled cost and latency — or a computer vision model running reliably at scale, I take it from idea to deployed system. What I build for clients: AI Agents & Multi-Agent Systems — autonomous, tool-using agents with LangChain, LangGraph, OpenAI & Groq RAG & LLM Apps — retrieval-augmented chatbots with FAISS vector databases, reranking, and hallucination-resistant answers Fine-Tuning & NLP Pipelines — domain-adapted LLMs, text classification, and AI-generated text detection Computer Vision — real-time object detection & tracking with YOLOv8, CLIP, and OpenCV Fintech & Predictive ML — XGBoost fraud detection, repayment prediction (AUC 0.9852), time-series forecasting Rapid AI MVPs — Streamlit dashboards and end-to-end prototypes that get to value fast Recent wins: Built a Binoculars-style LLM text detection system (Qwen2.5) for an AI content verification client Deployed a fintech repayment prediction model achieving AUC 0.9852 with XGBoost + SageMaker Autopilot Built a multi-agent finance automation system using LangChain + Streamlit Real-time object tracking system processing 30 FPS Reduced prediction errors by 40% for an e-commerce client Why clients trust me: BS in Artificial Intelligence (Air University, Islamabad) IEEE-published researcher — DAELocNet presented at AIIT 2025, Riyadh Top Rated on Upwork with 100% Job Success Score End-to-end ownership: data → model → deployment → clean handoff with documentation Core stack: Python · LangChain · LangGraph · OpenAI · Groq · RAG · FAISS · Vector Databases · Hugging Face · Fine-Tuning (LoRA/QLoRA) · YOLOv8 · CLIP · OpenCV · XGBoost · Time-Series Forecasting · Streamlit · FastAPI · PyTorch · Multi-Agent Systems · AI Integration Building an AI agent, RAG system, LLM app, or computer vision pipeline? Send me your brief — I'll outline the best technical approach and a clear path to production.

⭐⭐⭐⭐⭐ "Hussein was amazing! He's an outstanding developer with a passion for what he does. This guy loves software! He went above and beyond on my project doing so much more than I imagine another developer would do. He really invested in the project and brought a happy energy to the work. I am so grateful to have found someone so skilled and sincere. If you are looking for a developer who is great to work with on a personal level, and who is responsible and trustworthy, and who has outstanding technical skills, Hussein is your guy! Thank you!" - Adam Wes, The Bhakti Math Guru Welcome to my professional journey, spanning over 7+ years, where I've built a solid foundation in programming, problem-solving, and computer science. My enthusiasm for competitive programming led me to excel in ICPC qualifications and mentor Olympiad contenders. Beyond this, I've specialized in Artificial Intelligence, focusing on Large Language Models (LLMs), autonomous AI agents, and advanced AI solutions. This expertise has allowed me to contribute significantly to the fields of natural language processing and deep learning, driving innovative projects and solutions. My work is dedicated to harnessing the power of AI to solve complex challenges and create impactful technology. ✨ Core Areas of Expertise: ✨ ✅ Large Language Models (LLMs): Expert in building, training, and fine-tuning state-of-the-art LLMs for diverse applications, driving advancements in natural language understanding and generation. ✅ Autonomous AI Agents: Developing intelligent agents capable of making decisions and learning from their environment, setting new standards for autonomy and efficiency. ✅ Mixture of Experts (MoE): Leveraging MoE architectures to create highly specialized and scalable AI systems, optimizing for performance and adaptability. ✅ Computer Vision & Deep Learning: Adept in harnessing the power of deep learning for computer vision tasks, from object detection to image classification and beyond, transforming the way machines interpret the visual world. Additionally, I have a deep theoretical and practical knowledge of Data structures and Algorithms in the following areas: 🔷 Graphs: DFS, BFS, Dijkstra, Floyd Warshall, A*, MST, Max Flow, etc. 🔷 Data structures: Fenwick tree, Segment Tree, B-tree, BST, etc. 🔷 Searching and sorting: Binary search, Ternary search, KMP, Z, etc. 🔷 Math: probability theory, Number Theory, Computational Geometry, etc. 🔷 Dynamic Programming 🔷 Complexity and master theorem. 🏆 Recognitions & Achievements: 🏆 🥉 World Robot Olympiad 2016: Bronze Medalist 🎖️ Collegiate Programming Contest: Multiple Honorable Mentions (TCPC2015, TCPC2016, TCPC2017, SCPC2015, SCPC2017) 🏅 Academic Excellence: Outstanding achievements at Innopolis University, including a perfect GPA and top ranks in the Mathematics Olympiad. I am ready to give my best to make you satisfied with the results and the experience with me!

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!

Data science Expert/Machine Learning/Deep Learning/NLP/COMPUTER VISION. --5+ Years of Professional Experience in Machine Learning -- Specialized in Generative AI, Image Generation, and Speech Generation AI & Generative Model Specialist | 5+ Years of Experience in Building Intelligent Solutions I am an AI specialist with over 5 years of hands‑on experience in machine learning, deep learning, and generative AI, bringing a solid research background and real‑world expertise to deliver cutting‑edge, production‑ready solutions. My work focuses on transforming complex ideas into intelligent, scalable systems that drive real business value. What I Bring to the Table Generative AI Mastery: Advanced expertise in GANs, VAEs, diffusion models for image, video, and speech generation. --LLMs & Multimodal AI: Skilled in designing, fine‑tuning, and integrating large language models with multimodal capabilities (text, image, and speech). --End‑to‑End ML/DL Development: From architecture design and model training to deployment and continuous optimization across platforms. --Diverse AI Applications: Delivered solutions for text‑to‑image, image‑to‑image, speech‑to‑text, text‑to‑speech, and voice cloning, along with content generation, image captioning, visual understanding, and speech synthesis. --Deployment Expertise: Optimizing AI models for cloud, mobile, and edge devices using PyTorch, TensorFlow, and NVIDIA frameworks for speed and efficiency. --Research & Innovation Published research in Reinforcement Learning at ICML (“Inverse Constrained Reinforcement Learning”) with applications in human‑in‑the‑loop generative model improvement. --Expertise in Reinforcement Learning from Human Feedback (RLHF)—a powerful technique used in building next‑gen LLMs and generative models. --Technical Toolbox --Core Areas: Generative AI, Computer Vision, NLP, Speech & Audio Processing, Multimodal AI, OCR, Large Language Models, Big Data Analytics --Frameworks & Libraries: TensorFlow, PyTorch, Hugging Face, Keras, OpenAI, CoreML --Tools & Platforms: FastAPI, Flask, CUDA, OpenCV, TFLite --DevOps & Cloud: AWS, GCP, Kubernetes, Docker, GitHub, GitLab Looking for an AI partner who can design, build, and deploy innovative solutions? Let’s bring your vision to life — whether it’s AI‑powered content creation, smart automation, or custom multimodal solutions, I can deliver results that exceed expectation Thank you for taking the time to review my profile.. I am very passionate about being a competent and contributing team player. I look forward to answering your questions and opportunities. Regards. Maryam khan