I'm an AI Developer, Full-stack developer, and Solution Architect who builds automation that runs your ops like a digital employee, not a fragile trigger chain. As an AI Developer, I design each AI Agent to think, decide, and self-correct, so it works like a teammate, whether it powers an AI SaaS product or your daily ops, with Claude as the reasoning engine. That is the core promise of an AI Developer: systems that reason, not just react. A real AI Developer ships logic, not glue, and as a Full-stack developer I build the product around it end to end. Agentic AI is what turns that logic into a system. Most businesses don't have an automation problem; they have a system design problem, the kind a Solution Architect catches and an AI Developer solves. Workflows break on n8n, Make, and Zapier because no one engineered the logic underneath. I fix that with Agentic AI, powered by Claude, that holds up in production. That backbone runs through every AI SaaS I build, because the reasoning layer is the product, and the app around it has to be just as solid. Top 1% Upwork · 100% Job Success · 5.0 Rating · $100K+ Earned · 686+ Hours · 34+ Projects WHAT I BUILD: As an AI Developer, I give each tool one job: n8n for orchestration, Make for data transformation, Zapier for integrations, and Claude as the reasoning engine. As a Solution Architect, I make those systems qualify leads, sync HubSpot, Salesforce, and Airtable, and run support triage 24/7 with an AI Agent at the core. When you need a product, I ship full AI SaaS platforms on Claude, and as a Full-stack developer I own the frontend, backend, and database myself. This is the discipline an AI Developer and Solution Architect bring that an integrator cannot, which is why my Agentic AI work ships as a true AI Agent built to AI SaaS standards. Agentic AI is the difference between a script and a system. RECENT RESULTS: SaaS sales engine: an AI Agent on Claude cut lead response from 4 hours to under 4 minutes, $9K/month saved. Built as Agentic AI on Claude. Agency pipeline: one agent across 12 accounts saved 120 hours/month, 65% faster. Mapped by an AI Developer using Claude. Support triage: an AI Agent resolves 60% of Tier 1 tickets untouched, 80% faster, Claude at the reasoning layer. Built into an AI SaaS layer on Claude. AI app: React/Next.js, Node.js/PostgreSQL, shipped by a Full-stack developer with Claude in the core. Clients save 100-200+ hours/month, cut workload 30-60%, and hit ROI in 30 days, the return an AI Developer and Solution Architect produce with every system and AI SaaS they ship. TECH STACK: Automation: n8n · Make · Zapier AI & Agents: OpenAI · Claude (Anthropic) · LangChain · RAG · Agentic AI · AI SaaS design Backend: Node.js · Python · PostgreSQL Frontend: React · Next.js Integrations: HubSpot · Salesforce · Airtable · Shopify · WooCommerce HOW I WORK: As an AI Developer, I start with a free audit, then decide where each tool adds value and where the AI Agent, often Claude-driven, owns the decisions. What separates a real Solution Architect from one who just connects APIs is what separates a real AI Developer from an integrator: designing each AI Agent for the hard cases, failure edges, rate limits, malformed inputs, with self-correction built in. Agentic AI only earns trust when it survives those edge cases, and as a Full-stack developer I make sure the surrounding AI SaaS holds up too. Everything I deliver as a Solution Architect is stable, documented, and built to scale the way AI SaaS demands. As an AI Developer, I build each AI Agent once and build it right on Claude, so it runs without you. As a Full-stack developer, I stand behind the whole system, not just the glue between APIs. A dedicated AI Developer ships work you can trust, and your Solution Architect stands behind every system on Claude. A Solution Architect designs the system and a Full-stack developer builds the product, but an AI Developer who is also a Solution Architect and Full-stack developer covers all three, and is accountable when it ships. That is what Agentic AI, done right, demands. WHY AN AI DEVELOPER, NOT AN INTEGRATOR: An integrator connects apps; an AI Developer engineers decisions a Solution Architect would stand behind. An AI Developer plans for the day an API fails or an input arrives malformed, and builds the AI Agent to recover on its own; that is the Agentic AI edge. That reliability is what I'm paid for, and why I earn repeat work long after the first AI SaaS ships on Claude. An AI Developer who thinks like a Solution Architect and builds like a Full-stack developer is who you hire to stop rebuilding broken automations. Send your use case and I'll reply with a personalised Loom showing the AI Agent in action, a free audit from your AI Developer, Full-stack developer, and Agentic AI specialist on Claude. Bring the problem; your AI Developer brings the system.

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.

I build production-ready AI systems (RAG pipelines, LLM applications, and multilingual NLP) that solve real problems, not just demos that break in production. I'm a PhD researcher in AI/ML with 6+ years building NLP, machine translation, and information retrieval systems, with published work in ACM, Elsevier, EACL, and FIRE. That research background means I can handle the hard problems most freelancers can't: cross-lingual retrieval, low-resource and Indic languages, speech-to-speech systems, and LLM pipelines that actually hold up. What I can build for you: ✔ RAG pipelines and LLM-powered applications (LangChain, HuggingFace, PyTorch) ✔ NLP systems: text classification, NER, sentiment analysis, transformers ✔ Multilingual & cross-lingual AI, with deep expertise in Indic languages ✔ Speech-to-text and speech-to-speech AI ✔ Search and information retrieval engines ✔ Custom ML models: supervised, unsupervised, and deep learning ✔ End-to-end AI pipelines, from prototype to deployment I communicate clearly, hit deadlines, and care about code you can actually maintain. If you have an AI/NLP problem you're not sure is even solvable, that's exactly the kind of project I like. Send me a message and let's scope it.

6+ years of experience in designing and deploying production-grade LLM and RAG systems that reduce support costs, automate internal workflows, and deliver accurate, domain-specific answers . A results-driven AI practitioner with end-to-end experience architecting and deploying intelligent systems across research and production environments. Specialized in Generative AI - spanning RAG pipelines, agentic workflows, multimodal systems, and LLM-powered applications - with a strong foundation in classical deep learning across NLP, computer vision, and anomaly detection. Proven ability to translate cutting-edge research into scalable, cloud-native solutions on AWS, Azure, and GCP. Complements engineering depth with strategic and operational acumen, having driven AI integration initiatives at both startup and enterprise scale. What I Can Build for You • RAG-powered AI chatbots with private data (PDFs, databases, APIs) • Enterprise GenAI systems with role-based access & security • LLM-powered automation (document processing, email routing, analytics) • Speech-to-text + LLM pipelines (meetings, calls, compliance) • Cloud deployments on Azure / AWS / GCP with cost optimization If you’re looking for a reliable engineer who understands both AI and production systems, let’s start with a short call or a paid discovery to define the right architecture.

I have been building production AI systems for 6 years, and the one thing that has actually changed is speed. I am shipping code 100x faster than I was in 2019, without compromising on quality. Computer Science background, 6 years of hands-on experience building products and startups. If you want someone who treats your product like a business problem to solve, not a ticket to close, you are in the right place. I bring an engineering team behind me, so you get senior-level execution with the bandwidth of an agency when the project needs it. What I build: - AI Agents and Multi-Agent Systems (LangChain, LangGraph, CrewAI) - RAG Pipelines with Pinecone, PGVector, Weaviate - Voice AI Agents (Vapi, Retell, ElevenLabs, Deepgram, Twilio) - AI Chatbots and Conversational AI - LLM Applications (OpenAI, Claude, Llama, Groq, Ollama) - MCP Servers and Custom AI Tooling - Full-Stack AI SaaS Platforms - AI Automation Workflows (n8n, Python pipelines) - Computer Vision and Model Fine-Tuning - AI Deployment on AWS, GCP, Docker Industries I have shipped for: Fintech (Coinbase), Healthcare (DriveHealth), HVAC, Legal and Compliance, Education, SaaS startups, E-commerce. How I work: - Hourly contracts: I do the work myself, end to end. - Fixed-price contracts: I distribute work across my team in Islamabad. I stay personally accountable to you. Team members join calls only when their expertise is needed. Transparency is the rule here. My core stack: Python, LangChain, LangGraph, CrewAI, PyTorch, FastAPI, Next.js, React, PostgreSQL, PGVector, Pinecone, AWS, Docker, GitHub Actions. Why clients work with me: - Production-ready systems, not prototypes - Direct accountability, no project manager middle layer - On-time delivery backed by agile practices - Long-term code quality you can build on Send me a message with your project details. I reply within a few hours during business days and I am always up for a real conversation about cool projects with serious operators.

I build efficient, scalable data and AI solutions using Python, modern ML frameworks, and cloud-native tools. My work spans across data pipelines, ML systems, LLM apps, and cloud engineering. 🔧 What I Do • LLM Engineering: Prompt engineering, RAG pipelines, vector databases, embeddings, LLM integration (OpenAI, Anthropic, Azure OpenAI). • Python Development: Automation, backend APIs, microservices, FastAPI/Django. • Data Engineering: ETL/ELT pipelines, data modeling, Airflow, Kafka. • ML Engineering: Model training, evaluation, MLOps, deployment (TensorFlow, PyTorch, Scikit-learn). • Cloud & DevOps: AWS & Azure architectures, Docker, CI/CD, monitoring. • Automation: n8n workflows, API integrations. 💼 Skills & Tools Python, FastAPI, Django, LLM, RAG, Prompt Engineering, LangChain, n8n, Vector Databases, FAISS, Pinecone, RunPod, GPU Cloud, AWS, Azure, Docker, Airflow, Kafka, TensorFlow, PyTorch, Scikit-learn, Pandas, Elasticsearch, MongoDB, MySQL, CI/CD, MLOps, Data Pipelines

Hi, I’m Tallal, a 𝐏𝐡𝐃 𝐢𝐧 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐚𝐧 𝐀𝐈/𝐌𝐋 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 specializing in building production-ready Generative AI systems, LLM applications, NLP solutions, and data-driven software products using Python. I help startups and businesses turn ideas into real, working systems including 𝐋𝐋𝐌-𝐩𝐨𝐰𝐞𝐫𝐞𝐝 𝐚𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬, 𝐑𝐀𝐆-𝐛𝐚𝐬𝐞𝐝 𝐤𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐬𝐲𝐬𝐭𝐞𝐦𝐬, 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬, 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬, 𝐦𝐚𝐜𝐡𝐢𝐧𝐞 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐦𝐨𝐝𝐞𝐥𝐬, 𝐝𝐚𝐭𝐚 𝐬𝐜𝐢𝐞𝐧𝐜𝐞 𝐩𝐥𝐚𝐭𝐟𝐨𝐫𝐦𝐬, 𝐛𝐚𝐜𝐤𝐞𝐧𝐝 𝐬𝐲𝐬𝐭𝐞𝐦𝐬, 𝐚𝐧𝐝 𝐦𝐨𝐛𝐢𝐥𝐞 𝐚𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬. My focus is on building complete end-to-end AI products that combine intelligence + engineering + scalability, not just prototypes or models. 🚀 𝐖𝐇𝐀𝐓 𝐈 𝐁𝐔𝐈𝐋𝐃 • AI-powered web and mobile applications • 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 apps using 𝐆𝐏𝐓, 𝐂𝐥𝐚𝐮𝐝𝐞, 𝐆𝐞𝐦𝐢𝐧𝐢, 𝐚𝐧𝐝 𝐋𝐋𝐚𝐌𝐀 • 𝐑𝐀𝐆systems for search, knowledge bases, and AI assistants • 𝐀𝐈 agents and automation workflows for business processes • 𝐍𝐋𝐏 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 (chatbots, summarization, classification, sentiment analysis, extraction) • Machine learning models (prediction, recommendation, ranking, forecasting, analytics) • 𝐃𝐚𝐭𝐚 𝐬𝐜𝐢𝐞𝐧𝐜𝐞 pipelines (cleaning, feature engineering, visualization, modeling) • 𝐢𝐎𝐒 & 𝐀𝐧𝐝𝐫𝐨𝐢𝐝 𝐚𝐩𝐩𝐬 integrated with AI, APIs, payments, and notifications • Full-stack AI products with 𝐛𝐚𝐜𝐤𝐞𝐧𝐝, 𝐀𝐏𝐈𝐬, 𝐝𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬, 𝐚𝐧𝐝 𝐜𝐥𝐨𝐮𝐝 𝐝𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 • Backend automation systems for real-world operations 🧠 𝐀𝐈 / 𝐌𝐋 / 𝐃𝐀𝐓𝐀 𝐒𝐓𝐀𝐂𝐊 • Python, PyTorch, TensorFlow, Scikit-learn, Pandas, NumPy • 𝐆𝐏𝐓-𝟒, 𝐂𝐥𝐚𝐮𝐝𝐞, 𝐆𝐞𝐦𝐢𝐧𝐢, 𝐋𝐋𝐚𝐌𝐀 • LangChain, LangGraph, embeddings, vector databases • RAG pipelines, semantic search, prompt engineering, AI agents • NLP, classification, recommendation systems, predictive modeling • Data preprocessing, feature engineering, visualization 📱 𝐌𝐎𝐁𝐈𝐋𝐄 & 𝐁𝐀𝐂𝐊𝐄𝐍𝐃 𝐃𝐄𝐕𝐄𝐋𝐎𝐏𝐌𝐄𝐍𝐓 • iOS (𝐒𝐰𝐢𝐟𝐭), Android (𝐊𝐨𝐭𝐥𝐢𝐧), Flutter, React Native • FastAPI, Django, Flask, Node.js, REST APIs • PostgreSQL, MongoDB, Firebase • Authentication systems, admin dashboards, real-time features • Push notifications, API integrations, scalable backend systems ☁️ 𝐂𝐋𝐎𝐔𝐃 & 𝐏𝐑𝐎𝐃𝐔𝐂𝐓𝐈𝐎𝐍 𝐒𝐘𝐒𝐓𝐄𝐌𝐒 • AWS (Lambda, EC2, S3, DynamoDB, Cognito) • Docker, Firebase, CI/CD pipelines • Cloud deployment, monitoring systems • Production-grade AI/ML systems and scalable infrastructure ⭐ 𝐖𝐇𝐘 𝐂𝐋𝐈𝐄𝐍𝐓𝐒 𝐖𝐎𝐑𝐊 𝐖𝐈𝐓𝐇 𝐌𝐄 𝐈 𝐜𝐨𝐦𝐛𝐢𝐧𝐞 𝐀𝐈 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠, 𝐦𝐚𝐜𝐡𝐢𝐧𝐞 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠, 𝐚𝐧𝐝 𝐟𝐮𝐥𝐥-𝐬𝐭𝐚𝐜𝐤 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐭𝐨 𝐛𝐮𝐢𝐥𝐝 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐭𝐡𝐚𝐭 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐰𝐨𝐫𝐤 𝐢𝐧 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧. I don’t just train models; I build complete software systems, including: • 𝐁𝐚𝐜𝐤𝐞𝐧𝐝 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 • 𝐀𝐏𝐈𝐬 𝐚𝐧𝐝 𝐝𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬 • 𝐀𝐈 𝐥𝐨𝐠𝐢𝐜 𝐚𝐧𝐝 𝐦𝐨𝐝𝐞𝐥𝐬 • 𝐃𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝𝐬 𝐚𝐧𝐝 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 • 𝐌𝐨𝐛𝐢𝐥𝐞 𝐚𝐧𝐝 𝐰𝐞𝐛 𝐚𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 Everything is designed to be scalable, maintainable, and production-ready. I’ve worked across Generative AI systems, LLM integrations, RAG pipelines, NLP systems, machine learning models, data science workflows, mobile applications, backend platforms, automation systems, and real-time AI products. 𝐈𝐟 𝐲𝐨𝐮 𝐧𝐞𝐞𝐝 𝐬𝐨𝐦𝐞𝐨𝐧𝐞 𝐰𝐡𝐨 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐬 𝐀𝐈, 𝐦𝐚𝐜𝐡𝐢𝐧𝐞 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠, 𝐝𝐚𝐭𝐚 𝐬𝐜𝐢𝐞𝐧𝐜𝐞, 𝐛𝐚𝐜𝐤𝐞𝐧𝐝 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠, 𝐚𝐧𝐝 𝐦𝐨𝐛𝐢𝐥𝐞 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭, 𝐈 𝐜𝐚𝐧 𝐡𝐞𝐥𝐩 𝐲𝐨𝐮 𝐝𝐞𝐬𝐢𝐠𝐧 𝐚𝐧𝐝 𝐛𝐮𝐢𝐥𝐝 𝐭𝐡𝐞 𝐫𝐢𝐠𝐡𝐭 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧 𝐟𝐫𝐨𝐦 𝐢𝐝𝐞𝐚 𝐭𝐨 𝐝𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭.

Senior Data Scientist With 6 years of proven expertise in Machine Learning, Deep Learning, and building end-to-end machine learning solutions. I have a strong track record in delivering impactful data-driven insights and scalable models. Driven by my passion for problem-solving and innovation, I aim to leverage my knowledge, skills, and experience to take on new challenges, continuously advancing my abilities and contributing to the success of the team.

I am a dedicated ML / AI Engineer with over 2 years of experience in Python, Flask, FastAPI, Django, and Generative AI, continuously learning and honing my skills to stay at the cutting edge of technology. As a freelancer, I am committed to delivering high-quality solutions for my clients, bringing a strong foundation in Python development and diverse technical skills to each project. Expertise: • Python Development: Extensive experience in building and deploying applications using Python. • Web Frameworks: Proficient in Flask and FastAPI for developing scalable and efficient web applications. • Deep Learning & Machine Learning Frameworks: Skilled in PyTorch, TensorFlow, and Scikit-Learn for creating and optimizing machine learning models. • Data Science & Visualization: Expertise in Pandas, NumPy, Matplotlib, and OpenCV for data manipulation, analysis, and visualization. • Deployment & DevOps: Hands-on experience with Docker and Anaconda for creating reproducible environments and deploying applications. • Vector Databases: Knowledgeable in Pinecone and ChromaDB for managing and querying high-dimensional data. • Feature Store Management Services: Efficient management of reusable feature sets for robust machine learning pipelines. AI / ML Specializations: • Machine Learning Algorithms: Proficient in SVM, KNN, Regression, Decision Trees, Random Forest, Ensemble methods, and Time Series Analysis. • Deep Learning Architectures: Experience with ANN, CNN, YOLO, LSTMs, RNNs, Transformers and Transfer Learning for various AI applications. • LLM API Integration: Skilled in integrating OpenAI, Gemini, and Claude models using their APIs for production applications, covering prompt engineering, response optimization, rate-limit handling, and performance monitoring. • Generative Models: Proficient in working with diffusion models, GANs, and transformer-based generative architectures for image synthesis, inpainting, and style transfer, with experience in LoRA fine-tuning for model adaptation. • NLP & LLMs: Understanding of NLP tools such as Spacy, NLTK, NER, word2Vec, and TF-IDF; experience with LLMs like Gemini, GPT, Open Source LLMs for advanced language processing tasks. • Text-to-Speech & Speech-to-Text Models: Proficient in developing and fine-tuning speech processing applications, with hands-on experience utilizing Synthflow AI's customizable voice agents and ElevenLabs' advanced multilingual text-to-speech and speech-to-text technologies. • Retrieval-Augmented Generation (RAG): Proficient in integrating retrieval systems with generative models for enhanced AI-driven solutions, including hands-on experience with Multimodal RAG techniques that combine text, images, and audio inputs to improve contextual understanding and response accuracy. Feel free to contact me anytime to discuss how I can contribute to your project. Let's create something great together!

For more than a decade, I utilize the R and Python programming languages to process, visualize and extract information from data and for almost five years for Geospatial analysis. I'm the author / maintainer of R packages (I've submitted more than 10 to CRAN). You can view my Github profile at the following weblink: github.com/mlampros I work with R and Rstudio on a daily basis. I can work with machine learning algorithms based on almost all CRAN, Github or Gitlab repositories. I can utilize visualization R packages such as ggplot2, plotly, tmap, leaflet, mapview. I can create shiny applications (shiny.rstudio.com/gallery/) and report my results in .pdf, word, .html or any other available format using Rmarkdown. Moreover, I'm capable of using the hybrid 'Rcpp' and 'RcppArmadillo' R packages to improve the efficiency of R code and the Keras and Pytorch deep learning libraries for regression, classification, object detection or image segmentation (with or without pre-trained models).