# 🚀 AI Agents | Agentic AI | Enterprise AI | RAG | LangGraph | MCP | LLM Engineering | AI Automation I help startups and enterprises design, build, and deploy production-ready AI systems powered by Agentic AI, Large Language Models (LLMs), and intelligent automation. With **8+ years of experience** in Artificial Intelligence, Machine Learning, Data Engineering, and Full-Stack Development, I specialize in building autonomous AI agents, multi-agent systems, Retrieval-Augmented Generation (RAG) applications, AI copilots, and enterprise automation solutions that streamline operations and deliver measurable business value. ## 💡 Core Expertise ✅ AI Agents & Agentic AI Systems ✅ Multi-Agent Orchestration & Collaboration ✅ LangGraph, LangChain & LlamaIndex ✅ Model Context Protocol (MCP) & AI Tool Calling ✅ OpenAI, Claude, Gemini, Llama, DeepSeek, Mistral & Open-Source LLMs ✅ Retrieval-Augmented Generation (RAG) & Enterprise Knowledge Systems ✅ AI Copilots & Intelligent Assistants ✅ Voice AI Agents (VAPI, Retell AI, Bland AI, ElevenLabs) ✅ Workflow Automation (n8n, Make, Zapier, GoHighLevel) ✅ Vector Databases (Pinecone, Weaviate, Qdrant, ChromaDB, Milvus) ✅ Prompt Engineering, AI Evaluation & Guardrails ✅ AI SaaS Product Development ✅ Python, FastAPI, Django, Node.js & React ✅ Cloud Deployment (AWS, Azure & Google Cloud Platform) ✅ Docker, Kubernetes & CI/CD ## 🤖 AI Solutions I Build • Enterprise AI Assistants & Knowledge Chatbots • AI Customer Support & Helpdesk Agents • AI SDR & Sales Outreach Automation • AI Voice Calling & Appointment Booking Agents • Document Intelligence & OCR Automation • AI Contract Review & Legal Assistants • Research & Market Intelligence Agents • AI Recruiting & HR Automation • AI CRM & Business Process Automation • Financial Analysis & Reporting Assistants • Multi-Agent Business Workflows • Autonomous Task Execution Systems • AI Workflow Orchestration Platforms • Custom AI SaaS Applications ## ⚙️ Technology Stack **Agentic AI & LLMs** Agentic AI, AI Agents, Multi-Agent Systems, LangGraph, LangChain, LlamaIndex, CrewAI, AutoGen, DSPy, Semantic Kernel, MCP (Model Context Protocol), Prompt Engineering, AI Evaluation, Guardrails, RAG, Function Calling, Structured Outputs **Foundation Models** OpenAI GPT, Claude, Gemini, Llama, DeepSeek, Mistral, Qwen, Hugging Face Transformers Keywords:- AI Agents, Agentic AI, Retrieval-Augmented Generation (RAG), LangGraph, Model Context Protocol (MCP), Large Language Models (LLMs), LLM Engineering, AI Automation, Prompt Engineering, Context Engineering, Function Calling, Tool Calling, Multi-Agent Systems, Autonomous Agents, AI Workflows, Workflow Automation, AI Orchestration, Agent Memory, Vector Database, Embedding Models, Semantic Search, Hybrid Search, Dense Retrieval, Sparse Retrieval, Knowledge Graphs, GraphRAG, LangChain, LlamaIndex, CrewAI, AutoGen, Semantic Kernel, OpenAI API, Anthropic Claude, Google Gemini, Mistral AI, DeepSeek, Groq, Ollama, vLLM, TensorRT-LLM, Hugging Face Transformers, Sentence Transformers, FAISS, Pinecone, Weaviate, ChromaDB, Milvus, Qdrant, pgvector, Elasticsearch, BM25, Cross Encoder, Reranking, Context Window, Tokenization, Token Streaming, Structured Output, JSON Mode, Schema Validation, Guardrails AI, Prompt Chaining, Chain of Thought, ReAct, Tree of Thoughts, Self-Consistency, Reflection, Self-Correction, Planning Agents, Task Decomposition, Multi-Step Reasoning, AI Copilots, AI Assistants, Conversational AI, Natural Language Processing (NLP), Natural Language Understanding (NLU), Named Entity Recognition (NER), Intent Recognition, Text Classification, Sentiment Analysis, Document Parsing, OCR, Unstructured Data Processing, Knowledge Retrieval, Document Chunking, Metadata Filtering, Query Expansion, Context Compression, Long-Term Memory, Short-Term Memory, Session Memory, Human-in-the-Loop (HITL), AI Evaluation, LLMOps, MLOps, Prompt Versioning, Model Fine-Tuning, LoRA, QLoRA, PEFT, Quantization, Distillation, Inference Optimization, GPU Inference, Batch Inference, API Integration, REST API, GraphQL API, Webhooks, Server-Sent Events (SSE), FastAPI, Flask, Django, Python, TypeScript, Node.js, Docker, Kubernetes, Redis, RabbitMQ, Apache Kafka, Celery, Airflow, Temporal, n8n, Zapier, OpenTelemetry, LangSmith, Weights & Biases, MLflow, AI Observability, Agent Monitoring, Hallucination Detection, Prompt Injection Prevention, AI Safety, RBAC, OAuth 2.0, JWT Authentication, Secret Management, AWS Bedrock, Azure OpenAI, Google Vertex AI, Amazon SageMaker, Cloud Functions, Serverless AI, Edge AI, Multimodal AI, Vision Language Models (VLMs), Speech-to-Text, Text-to-Speech, Voice AI, Computer Vision, Diffusion Models, Synthetic Data, Knowledge Distillation, Fine-Grained Access Control, AI Governance, Explainable AI (XAI), Reinforcement Learning from Human Feedback (RLHF), Direct Preference Optimization (DPO), Agent Benchmarking, Retrieval Pipelines, Intelligent Automation, Generative AI

𝗧𝗵𝗲 𝗦𝘂𝗽𝗽𝗼𝗿𝘁 𝗔𝗴𝗲𝗻𝘁 𝗪𝗵𝗼 𝗧𝘆𝗽𝗲𝗱 𝘁𝗵𝗲 𝗦𝗮𝗺𝗲 𝗔𝗻𝘀𝘄𝗲𝗿 𝗧𝘄𝗶𝗰𝗲 A support agent at a mid-size company once told me she'd typed the same answer to the same question so many times she could do it in her sleep. Every day, another version of that question. Until one day, someone finally asked why a person was doing it at all. That's usually where I come in. Not with a pitch about AI. With a question about the thing nobody's counted yet. 𝙒𝙝𝙖𝙩 𝙔𝙤𝙪 𝘼𝙘𝙩𝙪𝙖𝙡𝙡𝙮 𝙉𝙚𝙚𝙙 (𝙄𝙩'𝙨 𝙋𝙧𝙤𝙗𝙖𝙗𝙡𝙮 𝙉𝙤𝙩 "𝘼𝙄") Most businesses that hire an AI developer don't need AI. They need one specific, annoying bottleneck to stop being their problem. AI is just the tool. The bottleneck is the target. So before anything technical happens, I spend time finding that target. Not guessing at it. Finding it. 𝙏𝙝𝙚 𝘾𝙖𝙩𝙝𝙚𝙙𝙧𝙖𝙡 𝙋𝙧𝙤𝙗𝙡𝙚𝙢 I think about a fine-tuned model a little like a cathedral. Medieval builders didn't just stack stone into a big room. They shaped the walls and ceiling so a single voice at the altar would carry clearly to the last pew, without turning into noise along the way. Most AI systems fail for the same reason bad architecture fails. They add noise instead of shaping signal. My job is building the room so the model's answer comes out clear, on tone, and grounded in what's actually true about your business. 𝙃𝙤𝙬 𝙄 𝘼𝙘𝙩𝙪𝙖𝙡𝙡𝙮 𝘽𝙪𝙞𝙡𝙙 𝙏𝙝𝙞𝙨 I build production LLM systems end to end: model selection, fine-tuning, evaluation, deployment, and continuous optimization after launch. A system that holds up under real traffic. 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 𝐌𝐨𝐝𝐞𝐥𝐬 I work directly with transformer-based foundation models using PyTorch, Hugging Face Transformers, and CUDA-accelerated training, choosing architecture based on your latency budget and data volume rather than defaulting to whichever model is trending. 𝐅𝐢𝐧𝐞-𝐭𝐮𝐧𝐢𝐧𝐠: LoRA and QLoRA for efficient adapter training, full fine-tuning when the use case genuinely justifies the cost, and domain adaptation so the model speaks your business, not generic internet text 𝐏𝐫𝐞𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭 RLHF and preference-tuning methods so the model's judgment, not just its facts, matches how your team actually makes decisions 𝐐𝐮𝐚𝐧𝐭𝐢𝐳𝐚𝐭𝐢𝐨𝐧: compressing models for cheaper, faster inference without losing the accuracy that makes them worth deploying Inference: serving infrastructure built for real throughput, not a single-request demo 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 Retrieval augmented generation (RAG) grounds answers in your actual documents instead of the model's memory, using vector databases like Pinecone, Weaviate, or pgvector. The part almost nobody talks about publicly: a bad embedding model will confidently retrieve the wrong document, and the language model will trust it completely, the same way people trust anything that simply sounds authoritative. I spend real time on embedding selection and chunking strategy before a single generation prompt gets written, because that's where most RAG systems actually succeed or fail. 𝐕𝐢𝐬𝐢𝐨𝐧 Computer vision systems for detection, classification, and embedding-based visual search at scale, and diffusion model work for generative image pipelines when the use case calls for it. 𝐌𝐮𝐥𝐭𝐢-𝐀𝐠𝐞𝐧𝐭 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 Multi-agent orchestration using frameworks like LangGraph, where a planner, a retriever, and a checker each handle their own piece of a workflow instead of one model trying to be everything at once. A single model attempting to plan, search, reason, and respond in one pass is a bit like one musician trying to play an entire orchestra alone. Technically there's sound. It isn't music. 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 𝐚𝐧𝐝 𝐌𝐋𝐎𝐩𝐬 A model sitting in a notebook is a research project. A model wrapped in a monitored, logged, rollback-ready API, with proper MLOps around training runs and versioning, is a product people can actually rely on. Cost-per-request monitoring, since an ungoverned AI bill is one of the fastest ways a promising pilot gets killed by finance Model evaluation pipelines tracking hallucination rate, latency, and cost as three separate numbers, catching regressions before your users do Guardrails and human checkpoints wherever a wrong answer is expensive: financial figures, customer-facing decisions, anything hard to undo Multimodal AI integration when a project needs text, vision, and structured data working together, not in isolation A model that sounds confident and is wrong causes more damage than a model that simply admits it doesn't know. I'd rather ship the one that knows its own limits. 𝐇𝐨𝐰 𝐈 𝐖𝐨𝐫𝐤 𝐖𝐢𝐭𝐡 𝐏𝐞𝐨𝐩𝐥𝐞 I ask more questions upfront than most freelancers bother to. Not to stall, but because a model trained on a misunderstood problem is expensive to unwind later. Retraining isn't like fixing a typo.

𝗠𝗼𝘀𝘁 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗲𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗔𝗜 𝗾𝘂𝗶𝗰𝗸𝗹𝘆 𝗿𝗲𝗮𝗹𝗶𝘇𝗲 𝗼𝗻𝗲 𝘁𝗵𝗶𝗻𝗴. 𝗔 𝗺𝗼𝗱𝗲𝗹 𝗮𝗹𝗼𝗻𝗲 𝗱𝗼𝗲𝘀𝗻’𝘁 𝗰𝗿𝗲𝗮𝘁𝗲 𝘃𝗮𝗹𝘂𝗲. 𝗪𝗵𝗮𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗶𝘀 𝗵𝗼𝘄 𝗶𝘁’𝘀 𝘁𝗿𝗮𝗶𝗻𝗲𝗱, 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗲𝗱, 𝗮𝗻𝗱 𝗱𝗲𝗽𝗹𝗼𝘆𝗲𝗱 𝗶𝗻 𝗿𝗲𝗮𝗹 𝘀𝘆𝘀𝘁𝗲𝗺𝘀. I specialize in building and deploying machine learning and Large Language Model (LLM) systems that solve real business problems, from intelligent assistants to data-driven automation and scalable AI services. With a background in machine learning engineering and software development, I focus on turning AI ideas into reliable, production-ready solutions. 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹 (𝗟𝗟𝗠) 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 Modern AI products rely on powerful language models, but they need proper architecture to perform well. 𝐈 𝐝𝐞𝐯𝐞𝐥𝐨𝐩 𝐋𝐋𝐌-𝐩𝐨𝐰𝐞𝐫𝐞𝐝 𝐚𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐬𝐮𝐜𝐡 𝐚𝐬: AI chatbots and assistants knowledge retrieval systems AI copilots for internal teams automated research tools intelligent data analysis assistants These systems combine LLM reasoning with external data sources, APIs, and business tools. Retrieval-Augmented Generation (RAG) Systems Many businesses want AI that can answer questions using their own internal data. 𝗜 𝗯𝘂𝗶𝗹𝗱 𝗥𝗔𝗚 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 𝘁𝗵𝗮𝘁 𝗰𝗼𝗻𝗻𝗲𝗰𝘁 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗺𝗼𝗱𝗲𝗹𝘀 𝘁𝗼: company documents and PDFs internal knowledge bases customer support data CRM systems business databases and APIs 𝗖𝗼𝗿𝗲 𝗰𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 𝗶𝗻𝗰𝗹𝘂𝗱𝗲: LangChain and LlamaIndex pipelines vector search infrastructure semantic retrieval optimization hallucination reduction strategies This allows AI systems to produce accurate answers grounded in company knowledge. 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 Beyond LLMs, I build and optimize traditional machine learning and deep learning models. 𝗧𝘆𝗽𝗶𝗰𝗮𝗹 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗶𝗻𝗰𝗹𝘂𝗱𝗲: predictive analytics models recommendation systems NLP and text analysis models classification and forecasting models AI-powered data processing pipelines The goal is always to create models that deliver measurable business insights. 𝗟𝗟𝗠 𝗙𝗶𝗻𝗲-𝗧𝘂𝗻𝗶𝗻𝗴 & 𝗠𝗼𝗱𝗲𝗹 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 Every AI system performs better when adapted to its specific task. 𝗜 𝘀𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘇𝗲 𝗶𝗻 𝗶𝗺𝗽𝗿𝗼𝘃𝗶𝗻𝗴 𝗟𝗟𝗠 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝘁𝗵𝗿𝗼𝘂𝗴𝗵: LoRA and QLoRA fine-tuning prompt engineering and optimization model evaluation and testing task-specific LLM adaptation structured output pipelines 𝗧𝗵𝗶𝘀 𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝘀 𝗮𝗰𝗰𝘂𝗿𝗮𝗰𝘆, 𝗿𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆, 𝗮𝗻𝗱 𝗱𝗼𝗺𝗮𝗶𝗻-𝘀𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲. 𝗠𝗟𝗢𝗽𝘀 & 𝗔𝗜 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 AI solutions must run reliably in production environments. I implement MLOps pipelines that ensure models remain scalable and maintainable. 𝗧𝗵𝗶𝘀 𝗶𝗻𝗰𝗹𝘂𝗱𝗲𝘀: model deployment pipelines experiment tracking with MLflow containerized inference services CI/CD for machine learning systems monitoring model performance and drift 𝗧𝗵𝗲𝘀𝗲 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀 𝗵𝗲𝗹𝗽 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀𝗲𝘀 𝗺𝗼𝘃𝗲 𝗳𝗿𝗼𝗺 𝗔𝗜 𝗽𝗿𝗼𝘁𝗼𝘁𝘆𝗽𝗲𝘀 𝘁𝗼 𝘀𝘁𝗮𝗯𝗹𝗲 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝘀𝘆𝘀𝘁𝗲𝗺𝘀. End-to-End AI System Development I support the full lifecycle of machine learning and AI projects. This includes: AI solution architecture and planning data preparation and model training backend AI service development API integrations with applications cloud deployment and monitoring The focus is always on building AI systems that integrate smoothly with existing products and workflows. 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗦𝘁𝗮𝗰𝗸 Programming & ML Frameworks Python, PyTorch, TensorFlow, Scikit-learn 𝗟𝗟𝗠 & 𝗔𝗜 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 LangChain, LlamaIndex, Hugging Face Fine-Tuning & Optimization LoRA, QLoRA, Prompt Engineering 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 Pinecone, FAISS, Weaviate, ChromaDB 𝗕𝗮𝗰𝗸𝗲𝗻𝗱 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 FastAPI, Flask, REST APIs 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 & 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝘃 Docker, Kubernetes AWS, Google Cloud, Azure 𝗪𝗵𝗮𝘁 𝗜 𝗛𝗲𝗹𝗽 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀𝗲𝘀 𝗕𝘂𝗶𝗹𝗱 AI chatbots and assistants knowledge base AI systems document analysis and processing tools predictive machine learning models AI automation workflows scalable AI backend services 𝗪𝗵𝘆 𝗖𝗹𝗶𝗲𝗻𝘁𝘀 𝗪𝗼𝗿𝗸 𝗪𝗶𝘁𝗵 𝗠𝗲 strong combination of ML research and engineering focus on production-ready AI systems scalable architectures designed for real use clear communication and reliable delivery practical solutions that create business impact If you’re building an AI product, machine learning system, or LLM-powered application, I’d be happy to discuss how we can implement the right solution. 𝗞𝗲𝘆𝘄𝗼𝗿𝗱𝘀 Machine Learning Engineer, LLM Engineer, Generative AI Developer, AI Model Development, RAG Pipeline Development, LangChain Developer, LlamaIndex Expert, LLM Fine Tuning, LoRA QLoRA, Pyth

AI/ML Engineer | NLP, LLM, Chatbot, Computer Vision, AI Automation System Specialist, RAG, n8n Engineering AI Systems That Actually Run in Production. I help startups, enterprises, and innovators unlock the true potential of AI by designing, building, and scaling intelligent automation systems. With over 6 years of experience in AI engineering, automation, and AI-driven product development, I specialize in AI Automation, AI Agents, Generative AI, and machine learning systems that solve real business challenges. [Updated: Jul 09, 2026] • 13,000+ hours of manual operations automated • $1.5M raised, Facia AI (biometric AI, Semi-Finalist Supernova) • 200+ countries served across deployed products • 15,000+ users onboarded on live production systems My work sits at the intersection of AI automation, AI agents, LLMs, and full-cycle product development. Whether you need an autonomous agent, a voice AI system, or a complete AI-powered workflow, I build robust, production-ready solutions tailored to your business goals. 𝗖𝗢𝗥𝗘 𝗘𝗫𝗣𝗘𝗥𝗧𝗜𝗦𝗘 & 𝗗𝗘𝗟𝗜𝗩𝗘𝗥𝗔𝗕𝗟𝗘𝗦 1️⃣ AI AUTOMATION & WORKFLOWS I build end-to-end AI automation systems using n8n, Make, and Zapier, connecting your CRM, data sources, and business tools into one seamless workflow. This includes custom API pipelines and automated processes that handle real operational work, not just simple triggers. 2️⃣ AI AGENTS & VOICE AGENTS I build LLM-powered AI agents and voice agents using LangGraph, AutoGen, and CrewAI for multi-step reasoning and task execution, plus Vapi, Retell, and ElevenLabs for voice. These are integrated with scheduling, CRM syncing, and multi-step decision flows for sales, support, and operations, handling real conversations and real tasks, not scripted bots. 3️⃣ LLM & RAG CHATBOTS I build chatbots and knowledge systems using RAG and LangChain with vector databases, so teams get accurate, grounded answers from their own data instead of guesses. 4️⃣ MACHINE LEARNING I develop machine learning and deep learning models for prediction and classification, trained to handle real, messy data rather than clean benchmarks, using TensorFlow and PyTorch. 5️⃣ COMPUTER VISION I build computer vision systems including OCR and object detection, designed for real-world inputs like scanned documents and live image data. All of the above, from machine learning to computer vision to automation, is backed by scalable Python and FastAPI backends for production deployment. 𝗖𝗢𝗥𝗘 𝗦𝗧𝗔𝗖𝗞 AI Automation: n8n, Zapier, Pipedream, Make. com integrations for CRMs, ERPs, SaaS tools LLMs: OpenAI GPT, Claude, Gemini AI Systems & Agents: RAG, LangChain, LangGraph, AutoGen, CrewAI, Vector Databases, AI Agents, Agentic Workflows Voice agent: Vapi, Retell, ElevenLabs, Twilio Machine Learning & Deep Learning: TensorFlow, PyTorch Computer Vision: OpenCV, OCR, Object Detection Backend: Python, FastAPI, REST APIs Databases: Supabase, PostgreSQL, MongoDB, Redis 𝗛𝗢𝗪 𝗜 𝗪𝗢𝗥𝗞 I offer a Free 30-minute consultation where I'll map out exactly how AI automation, agents, machine learning, or computer vision can be applied to your specific workflow, no fluff, just a clear action plan you can use immediately whether you hire me or not. I'm equally comfortable working with founders making fast decisions and technical teams that need precision. Clear communication, fast response, and reliable delivery, with full visibility into your project at every step. 📩 𝗙𝗲𝗲𝗹 𝗳𝗿𝗲𝗲 𝘁𝗼 𝗿𝗲𝗮𝗰𝗵 𝗼𝘂𝘁 𝗮𝗻𝘆𝘁𝗶𝗺𝗲 𝘁𝗼 𝗱𝗶𝘀𝗰𝘂𝘀𝘀 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝗻𝗲𝗲𝗱𝘀. 𝗜’𝗺 𝗮𝘃𝗮𝗶𝗹𝗮𝗯𝗹𝗲 24/7 𝗳𝗼𝗿 𝗰𝗼𝗻𝘀𝘂𝗹𝘁𝗮𝘁𝗶𝗼𝗻𝘀 𝗮𝗻𝗱 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻. AI Automation, AI Agents, Agentic AI, Agentic Workflows, AI Agent Development, AI Workflow Automation, n8n, Make, Zapier, Pipedream, Process Automation, Voice Agents, Voice AI, AI Chatbot Development, Conversational AI, LLM, Large Language Models, RAG, Retrieval Augmented Generation, LangChain, LangGraph, AutoGen, CrewAI, Multi-Agent Orchestration, Vector Databases, Machine Learning, Deep Learning, Predictive Modeling, Computer Vision, OCR, Object Detection, Image Classification, TensorFlow, PyTorch, OpenCV, Natural Language Processing, NLP, Python, FastAPI, REST APIs, AI Integration, AI Product Development, AI SaaS Development

🏆 𝐄𝐱𝐩𝐞𝐫𝐭-𝐕𝐞𝐭𝐭𝐞𝐝 — 𝐓𝐨𝐩 𝟏% 𝐨𝐟 𝐔𝐩𝐰𝐨𝐫𝐤 𝐓𝐚𝐥𝐞𝐧𝐭 💰 $𝟓𝟎𝟎𝐊+ 𝐄𝐚𝐫𝐧𝐢𝐧𝐠𝐬 | 𝟖𝟎+ 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬 | 𝟖,𝟎𝟎𝟎+ 𝐇𝐨𝐮𝐫𝐬 ⭐ 𝟏𝟎𝟎% 𝟓-𝐒𝐭𝐚𝐫 𝐑𝐞𝐯𝐢𝐞𝐰𝐬 | 𝐙𝐞𝐫𝐨 𝐍𝐞𝐠𝐚𝐭𝐢𝐯𝐞 𝐅𝐞𝐞𝐝𝐛𝐚𝐜𝐤 ☁️ 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐞𝐝 𝐀𝐖𝐒 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭 I am a 𝐋𝐞𝐚𝐝 𝐀𝐈/𝐌𝐋 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 with 10+ 𝐲𝐞𝐚𝐫𝐬 of experience across 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠, 𝐍𝐋𝐏, 𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠, 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈, 𝐋𝐋𝐌𝐬, 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬, 𝐕𝐨𝐢𝐜𝐞 𝐀𝐠𝐞𝐧𝐭𝐬, and production AI engineering. Clients rely on me to build 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧-𝐫𝐞𝐚𝐝𝐲 𝐀𝐈 𝐬𝐲𝐬𝐭𝐞𝐦𝐬- not just demos or API wrappers. My focus on reliability, scalability, security, and measurable business outcomes has helped me maintain 𝟏𝟎𝟎% 𝟓-𝐬𝐭𝐚𝐫 𝐫𝐞𝐯𝐢𝐞𝐰𝐬 with no negative feedback on Upwork, a track record rarely seen among freelancers with a comparable volume of completed work. I can develop a complete 𝐞𝐧𝐝-𝐭𝐨-𝐞𝐧𝐝 𝐀𝐈 𝐩𝐫𝐨𝐝𝐮𝐜𝐭- from solution architecture and model development to backend, frontend, cloud deployment, monitoring, and scaling- or integrate an AI solution directly into your existing applications and business workflows. 🎙️ 𝐀𝐈 𝐕𝐨𝐢𝐜𝐞 𝐀𝐠𝐞𝐧𝐭𝐬 ➜ Built and productionized multiple real-time AI voice agents using 𝐋𝐢𝐯𝐞𝐊𝐢𝐭 ➜ AI voice receptionists, customer support agents, sales agents, appointment-booking agents, and voice assistants ➜ Low-latency speech-to-speech conversations, natural turn-taking, interruption handling, and voice activity detection ➜ Function calling, call routing, telephony integration, human handoff, and workflow automation ➜ Integration with STT, TTS, LLMs, APIs, CRMs, databases, and enterprise knowledge bases ➜ LiveKit Agents, Deepgram, OpenAI Realtime, ElevenLabs, Amazon Polly, Claude, and AWS Bedrock 🤖 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 & 𝐋𝐋𝐌 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 ➜ Agentic AI systems using LangGraph, AutoGen, CrewAI, and custom orchestration frameworks ➜ Multi-agent workflows, tool calling, memory, planning, human-in-the-loop, and autonomous task execution ➜ Custom AI chatbots and copilots using OpenAI, Claude, AWS Bedrock, Llama, Mistral, and Qwen ➜ RAG pipelines, semantic search, hybrid retrieval, reranking, vector databases, and knowledge assistants ➜ Document intelligence, natural-language-to-SQL, structured data extraction, and workflow automation ➜ LLM evaluation, guardrails, prompt engineering, structured outputs, and hallucination reduction 📊 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 & 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 ➜ Predictive modelling, classification, regression, clustering, and anomaly detection ➜ Time-series forecasting, demand forecasting, customer segmentation, and churn prediction ➜ Recommendation engines, ranking systems, personalization, and similarity matching ➜ Sentiment analysis, text classification, topic modelling, summarization, and information extraction ➜ Computer vision, object detection, image classification, motion tracking, and scene recognition ➜ Feature engineering, model evaluation, explainable AI, experimentation, and MLOps 🧠 𝐋𝐋𝐌 𝐅𝐢𝐧𝐞-𝐓𝐮𝐧𝐢𝐧𝐠 & 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 ➜ Fine-tuning LLMs for domain adaptation, Q&A, classification, extraction, legal, medical, and enterprise use cases ➜ Synthetic dataset generation, training-data preparation, and evaluation frameworks ➜ LoRA, QLoRA, supervised fine-tuning, and instruction tuning ➜ Production deployment using vLLM, Hugging Face, AWS, GCP, RunPod, Docker, and serverless infrastructure ☁️ 𝐀𝐖𝐒 & 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐀𝐈 ➜ AWS Bedrock, SageMaker, Lambda, API Gateway, ECS, ECR, S3, RDS, DynamoDB, and OpenSearch ➜ Secure, scalable, multi-tenant AI applications and data pipelines ➜ Python, FastAPI, PostgreSQL, Redis, MongoDB, and vector databases ➜ Monitoring, model evaluation, latency optimization, cost control, and production support Whether you need a complete 𝐀𝐈 𝐒𝐚𝐚𝐒 𝐩𝐫𝐨𝐝𝐮𝐜𝐭, an 𝐀𝐈 𝐜𝐨𝐩𝐢𝐥𝐨𝐭, a 𝐯𝐨𝐢𝐜𝐞 𝐚𝐠𝐞𝐧𝐭, a predictive ML system, or an AI capability integrated into your existing workflow, I can take it from idea to a secure, scalable, and production-ready solution.

🚀Principal AI/ML Engineer — 13 years · Expert-Vetted (Top 1% on Upwork) I build production AI systems end to end — from the bare-metal GPU infrastructure all the way up to the product your users actually touch. Not prototypes that die in a notebook: real, deployed, monitored systems that hold up under load. For 13 years I've designed, trained and shipped AI across large language models, generative AI, computer vision, speech and NLP — for clients in insurance, healthcare, finance, public safety and media. Today I run the on-prem GPU infrastructure behind a national AI platform, serving a 753-billion-parameter language model (GLM-5.2) across a 32-GPU NVIDIA B200 cluster that handles 1,000+ users at once. ⭐ What I can do for you 🔹 LLM serving & cost optimization — Kill your per-token API bill. Self-host open models (GLM, Qwen, Llama, Mistral, Kimi) on your hardware or cloud with vLLM/SGLang: tensor and expert parallelism, FP8 quantization, high concurrency, low latency, predictable cost. 🔹 Fine-tuning & custom models — SFT, LoRA / QLoRA and full fine-tunes tailored to your domain and language, including hard low-resource languages. (I fine-tuned Whisper for Uzbek to 10.5% WER and built custom neural TTS voices from scratch.) 🔹 RAG & knowledge systems — RAG with hybrid dense+sparse retrieval, rerankers, vector and graph DBs (Qdrant, Pinecone, Neo4j, NebulaGraph), plus evaluation so you can prove it got better. 🔹 Computer vision — Vision and speech, not just chatbots. Face analytics, object detection and tracking, OCR and document pipelines, health-from-video (heart rate, SpO2, BMI), real-time multi-camera video, streaming voice agents, AI calling systems, voice cloning, image and video generation. 🔹 AI agents & automation — Multi-agent systems, document processing, LLM-powered classification and extraction, and end-to-end workflow automation. 🔹 Generative AI — Image and video generation, avatars, voice cloning, text-to-speech and speech-to-text. GPU infrastructure that stays up. Bare-metal and cloud clusters, CUDA, multi-node inference, autoscaling, load balancing, monitoring, CI/CD. The layer most freelancers leave you holding. 🚦 HOW ENGAGEMENTS START • Cost audit — I look at what you spend on tokens or cloud GPUs and tell you what self-hosting or a smaller fine-tune would actually cost. • Prototype-to-production rescue — it works in a notebook or a demo; I make it survive real users, real load, real uptime. • Build from zero — model choice, infra, backend, deployment, monitoring, handover docs. ✳️ HOW I WORK • We agree on the target first: latency, cost per request, accuracy, concurrency. Numbers, not vibes. • I build inside your environment: your cloud or your hardware, your repo, your keys. You own all of it. • You get infrastructure-as-code, a runbook and working monitoring, not a model file and good luck. • You talk to the person writing the code. No account manager, no team behind me. • Straight answers. If a smaller model, cheaper hardware or no AI at all is the right call, I say so before you spend. 🛠️ STACK LLM/RAG: GPT-4.1/4o/o3, Claude 4, Gemini 2.5, Llama 3.x, Qwen 3, DeepSeek, Mistral, GLM, fine-tuning, hybrid and semantic search, prompt and context engineering, structured output, knowledge-graph RAG Agents: LangGraph, LangChain, LlamaIndex, CrewAI, AutoGen, OpenAI Agents SDK, DSPy, Haystack, MCP, function calling, long-term memory, human-in-the-loop Serving & GPU: vLLM, SGLang, Ollama, NVIDIA NIM, CUDA, quantization, autoscaling, load balancing, cluster ops ML: PyTorch, TensorFlow, Hugging Face, Transformers Speech: Whisper, Faster-Whisper, speech recognition, TTS, speaker recognition, real-time voice Vision & generative AI: YOLO, ViT, OCR, segmentation, medical imaging, multimodal, image and video generation, avatars Backend: Python, FastAPI, Django, Flask, REST/GraphQL/WebSockets/gRPC, Celery, Redis, Kafka, RabbitMQ, Airflow Data: PostgreSQL, MySQL, MongoDB, Elasticsearch, Neo4j, Pinecone, Weaviate, Qdrant, Milvus, ChromaDB, FAISS Infra: AWS, GCP, Azure, RunPod, Hetzner, Vast.ai, Docker, Kubernetes, Helm, NGINX, Traefik, GitHub Actions, Linux Integrations: OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Twilio, WhatsApp Business, Slack, Salesforce, HubSpot, Stripe, OAuth2/JWT 📦 TYPICAL BUILDS Enterprise assistants and internal copilots · RAG and knowledge platforms · support, sales and SDR bots Voice assistants and AI calling · document AI and OCR pipelines · medical and financial AI Predictive analytics and recommenders · computer vision apps · workflow automation · AI dashboards 📩 Send me a message with what you're building and where it's stuck. One paragraph is enough. I'll tell you honestly whether I can help and exactly how I'd approach it, before you spend a dollar. If I'm not the right person, I'll say so.

ABOUT: 🔬 Research cited by Qwen (Alibaba's frontier LLM) 🏆 Top 5% in Machine Learning on Upwork ⚡ 7+ years building AI systems that actually ship 👔 Notable clients: Giotto.ai · EDF Compliance · IKM · Data Mania CERTIFICATIONS: ✔️ Stanford Online — Machine Learning EXPERIENCE & SKILLS: 📊📈🕵🏻‍♀️ AI Agent Development, Generative AI, Large Language Models (LLM), Claude API, AI Integration, OpenAI API, Anthropic, LLM Fine-tuning, Retrieval Augmented Generation (RAG), Prompt Engineering, LLM Prompt Engineering, LangChain, Multi-modal AI, Natural Language Processing (NLP), Voice Agents, AI Voice Agent, Text-to-Speech, Speech-to-Text, Whisper AI, ElevenLabs, Conversational AI, Chatbot Development, AI App Development, Agentic AI, Computer Vision, Object Detection, Image Segmentation, Object Tracking, Video Analysis, OCR, TensorFlow, PyTorch, Machine Learning, Deep Learning, Python, JavaScript, React, Next.js, Node.js, TypeScript, PostgreSQL, Supabase, MongoDB, REST API Integration, API Development, Full-Stack Development, SaaS Development, Web Application Development, Backend Development, AWS, GCP, Vercel, Docker, Google Ads, Google Sheets, Google Analytics, Marketing Automation, Workflow Automation, Business Process Automation, Data Pipelines, Data Science, Data Engineering.

I build AI systems that work in production - not just in demos. With 10+ years in AI/ML and enterprise software, 3000+ hours on Upwork, 700+ solutions delivered, and 400+ clients across the globe, I've earned a simple reputation: if you need intelligent automation, a custom LLM application, or a scalable ML pipeline, I'll build it, ship it, and make sure it holds up. I work across the full AI stack: from raw data ingestion and model training to fine-tuning LLMs, deploying RAG architectures, and integrating everything into production-grade systems. Whether the project lives on AWS (Bedrock, SageMaker, Textract, Comprehend), Google Cloud (Vertex AI), or runs locally (Ollama, LLaMA, DeepSeek), I build for the environment that fits your business, not the one that's easiest for me. ➛ 𝗪𝗵𝗮𝘁 𝗜 𝗯𝘂𝗶𝗹𝗱 𝗠𝗟𝗢𝗽𝘀 & 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗠𝗟 End-to-end ML pipelines with MLflow, SageMaker, and Azure ML. Model training, fine-tuning, versioning, and monitoring. TensorFlow, PyTorch, Keras, Scikit-learn, XGBoost. Deep learning, neural networks, and Diffusion Models. 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 & 𝗟𝗟𝗠 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 Custom GPTs, AI agents, and RAG pipelines using OpenAI, Claude, LLaMA, Mistral, and DeepSeek. LLM fine-tuning with LoRA/QLoRA. Prompt engineering (zero-shot, few-shot, chain-of-thought). Multi-agent systems with LangChain and LangGraph. Deployed on AWS Bedrock, Vertex AI, or self-hosted via Ollama/Supabase. 𝗗𝗮𝘁𝗮 & 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 Data mining, web scraping, and pipeline engineering with Pandas, NumPy, and Python. Business intelligence and analytics with Amazon QuickSight. NLP with Amazon Comprehend, BERT, SpaCy, Transformers - text classification, sentiment analysis, entity recognition, content generation. 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 & 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 OCR and document processing with Amazon Textract, Azure Computer Vision, OpenCV, and Tesseract. Image recognition, face detection, and vision-based automation. Stable Diffusion and generative image workflows. 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗔𝗜 & 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 AI assistants and knowledge-base chatbots. Context-aware conversation systems integrated via API. GoHighLevel automation and CRM-connected AI workflows. Amazon Translate for multilingual deployments. 𝗪𝗵𝘆 𝗰𝗹𝗶𝗲𝗻𝘁𝘀 𝗰𝗼𝗺𝗲 𝗯𝗮𝗰𝗸 Most AI projects fail at the handoff from prototype to production. I've spent a decade closing that gap, writing systems that are maintainable, monitored, and built to scale beyond the first deployment. - Production-first architecture from day one - Strong documentation and clean, handoff-ready code - Experience across AWS, Azure, GCP, and open-source stacks - Clear communication throughout, no black boxes - On-time delivery with post-launch accountability 𝗘𝘃𝗲𝗿𝘆 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝗶𝗻𝗰𝗹𝘂𝗱𝗲𝘀: - 1 month post-delivery support - 1 month warranty on all deliverables - Dedicated technical consultation If you're building an AI product, automating a complex workflow, or turning your data into something that actually makes decisions, let's talk. I'll tell you in the first conversation whether it's feasible, how long it takes, and what it'll cost.

I build production-grade 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐚𝐧𝐝 𝐢𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐭 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 that achieve 𝟲𝟬–𝟴𝟬% 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 in live enterprise environments across healthcare, fintech, recruitment, and operational workflows. With a 𝐏𝐡.𝐃. 𝐟𝐫𝐨𝐦 𝐭𝐡𝐞 𝐔𝐧𝐢𝐯𝐞𝐫𝐬𝐢𝐭𝐲 𝐨𝐟 𝐒𝐭𝐮𝐭𝐭𝐠𝐚𝐫𝐭 (𝐆𝐞𝐫𝐦𝐚𝐧𝐲) and a Habilitation from 𝐆𝐞𝐨𝐫𝐠𝐢𝐚 𝐓𝐞𝐜𝐡 (𝐔𝐒𝐀), I bring a rare combination of real-world production engineering experience and deep academic research expertise in 𝐚𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥 𝐢𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 𝐬𝐲𝐬𝐭𝐞𝐦𝐬. My work combines advanced AI orchestration (LangGraph, CrewAI) with robust ML engineering (fine-tuning, predictive scoring) to ensure reliability. Whether you need autonomous agents, voice AI, or intelligent automation, I focus on measurable outcomes: reduced costs, faster workflows, and traceable decision-making. 𝐂𝐨𝐫𝐞 𝐄𝐱𝐩𝐞𝐫𝐭𝐢𝐬𝐞 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 & 𝐌𝐮𝐥𝐭𝐢-𝐀𝐠𝐞𝐧𝐭 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 Autonomous systems that reason, plan, and execute real tasks: • MCP-based orchestration routing tasks across sub-agents in real time. • Stateful flows with conditional routing, persistent memory, and LangSmith monitoring. • Human-in-the-loop checkpoints before any irreversible action fires. • Full action logging and automatic. 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬: LangGraph, CrewAI, AutoGen, OpenAI Agents, Claude, MCP, LangSmith 𝐑𝐀𝐆 & 𝐋𝐋𝐌 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 • Enterprise-grade knowledge assistants. • Structure-aware chunking, hybrid semantic and metadata search. • Vector database integration for scalable retrieval • Hybrid search and structured context systems 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬: Pinecone, Weaviate, Qdrant, LlamaIndex, OpenAI, Claude, Mistral, graph-based retrieval 𝐕𝐨𝐢𝐜𝐞 𝐀𝐈 & 𝐂𝐨𝐧𝐯𝐞𝐫𝐬𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 • AI voice agents for sales, support, and operations • Real-time call reasoning and response generation • CRM integration and post-call automation • Speech-to-text and text-to-speech pipelines 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬: VAPI, Twilio, ElevenLabs, Whisper, LiveKit 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐭 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 & 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 • End-to-end process automation (60–80% efficiency gain) • Event-driven pipelines connecting CRMs, APIs, and AI agents • Human-in-the-loop workflows for critical business decisions 𝐓𝐨𝐨𝐥𝐬: n8n, Make, Zapier, FastAPI, Webhooks 𝗔𝗜-𝗣𝗼𝘄𝗲𝗿𝗲𝗱 𝗙𝗿𝗼𝗻𝘁𝗲𝗻𝗱 & 𝗜𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲𝘀 • React, Next.js, Tailwind for AI dashboards and agent portals • Real-time agent status monitoring and control interfaces • Client-facing AI interaction layers 𝐌𝐋 𝐏𝐨𝐰𝐞𝐫𝐞𝐝 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬 • Predictive scoring for agent routing and prioritisation • Fine-tuned models for domain-specific accuracy • Churn prediction, fraud detection, demand forecasting • Data pipelines ensuring clean input for RAG systems 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬: Python, XGBoost, Hugging Face, PostgreSQL, scikit-learn, MLflow 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗮𝗹 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲𝘀 • Deterministic and controlled agent workflows • Traceable retrieval systems with structured outputs • Hybrid architectures combining LLMs + external tools • Human-in-the-loop safety for critical operations • Logging, observability, and failure handling built-in 𝐖𝐡𝐚𝐭 𝐈 𝐃𝐞𝐥𝐢𝐯𝐞𝐫 • Production-ready AI agent systems • Enterprise RAG and knowledge systems • End-to-end automation pipelines • API-driven AI integrations • Scalable, maintainable AI architectures 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 𝐒𝐭𝐚𝐜𝐤 • 𝗟𝗟𝗠𝘀: Claude, GPT-4o, Gemini, Mistral, open-source models via Hugging Face routed across providers based on latency and cost tradeoffs using OpenRouter • 𝗔𝗴𝗲𝗻𝘁 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀: LangGraph, CrewAI, LangChain, AutoGen, MCP orchestration • 𝗥𝗔𝗚 𝗦𝘆𝘀𝘁𝗲𝗺𝘀: Pinecone, Weaviate, Qdrant, Milvus, graph-based retrieval, hybrid semantic + metadata search • 𝐕𝐨𝐢𝐜𝐞 & 𝐂𝐨𝐧𝐯𝐞𝐫𝐬𝐚𝐭𝐢𝐨𝐧𝐚𝐥: VAPI, ElevenLabs, Twilio, Whisper, LiveKit • 𝗙𝗶𝗻𝗲-𝘁𝘂𝗻𝗶𝗻𝗴 & 𝗔𝗱𝗮𝗽𝘁𝗮𝘁𝗶𝗼𝗻: LoRA, QLoRA, domain-specific model evaluation, Hugging Face deployment pipelines • 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 & 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧: n8n, Make, Zapier, webhook pipelines, REST APIs, HubSpot, Salesforce, Gmail, Microsoft 365, GHL • 𝐁𝐚𝐜𝐤𝐞𝐧𝐝: Python, FastAPI, TypeScript, PostgreSQL, Redis, Docker, CI/CD • 𝐅𝐫𝐨𝐧𝐭𝐞𝐧𝐝 (AI Interfaces) : React, Next.js, Tailwind CSS for AI-powered dashboards, operational interfaces, and client-facing agent portals • 𝗖𝗹𝗼𝘂𝗱 & 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲: AWS, Azure, GCP, Docker, Kubernetes, CI/CD pipelines 𝐋𝐞𝐭’𝐬 𝐛𝐮𝐢𝐥𝐝 𝐚 𝐬𝐜𝐚𝐥𝐚𝐛𝐥𝐞 𝐀𝐈 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧 𝐭𝐡𝐚𝐭 𝐝𝐞𝐥𝐢𝐯𝐞𝐫𝐬 𝐫𝐞𝐚𝐥 𝐑𝐎𝐈.

Hello 👋 I’m Marlon — a senior-level AI developer, machine learning architect, and founder of Starbourne Labs. I specialize in building and scaling real-world, production-grade AI systems — from advanced LLM-powered agents to GPU-optimized infrastructure and real-time ML pipelines. 🚀 RESULTS • Delivered 20+ AI/ML products, helping founders raise over $30 million in VC funding • Scaled Merciv.ai to $3M+ ARR using multi-tenant LLM microservices with sub-300ms latency • Built a GPT-4o support agent at Form Labs handling over 2 million messages per month • Deployed LLM chatbots serving 50,000+ users across 12 industries • Reduced inference costs by 70% using quantization (QLoRA, INT8, AWQ) and serverless GPU orchestration • Boosted agent accuracy by 40% with better tool-use, memory routing, and embedding optimization 🤖 LLM Chatbots & Agentic Systems • GPT-4o, Claude 3 Opus, Gemini 1.5, Llama 3, Mistral • LangGraph, LangChain, AutoGen, CrewAI, OpenAI Tools • Tool use, function calling, memory, persona control, ReAct workflows • RAG pipelines with pgvector, Qdrant, Pinecone, Weaviate • API-integrated agents with autonomous task chaining and multi-step reasoning ⚙️ Full-Stack AI Platforms • Python, Go, TypeScript, React, Next.js, FastAPI, Flask • REST, gRPC, GraphQL microservices • PostgreSQL, MongoDB, Neo4j, Redis • Edge deployment via Docker, Kubernetes, RunPod, Modal, Beam.cloud 🧠 Machine Learning & MLOps • MLflow, DVC, Ray, Kubeflow, Airflow, ArgoCD • End-to-end CI/CD for model training, tuning, testing, and deployment • Serverless GPU pipelines with Triton, ONNX Runtime, DeepSpeed • Multi-model orchestration with autoscaling and cost-aware inference 🔍 Model Engineering & Optimization • Time-series: LSTM, TFT, DeepAR for forecasting and anomaly detection • NLP with GPT-4o, Claude, Gemini, Llama 3 + instruction tuning • Fine-tuning: LoRA, QLoRA, DPO, PEFT, Functionary • Evaluation using Promptfoo, Trulens, Helm for agent scoring and regression • Embedding-based search, classification, clustering, and content tagging 💼 Real-World Use Cases Delivered • Autonomous AI agents for research, documentation, and scheduling • Multimodal copilots (text + vision/audio) using OpenAI Vision, Whisper, LLaVA • Fintech AI for real-time fraud detection, credit scoring, and forecasting • Healthcare agents for intake triage, clinical summarization, and document routing • AI copilots for product QA, internal tools, legal review, and CX automation ✅ WHY HIRE ME 1. Deep technical expertise in today’s most powerful LLMs and ML tools 2. AI-native product mindset — optimized for impact, cost, and user experience 3. MVPs delivered in 6–8 weeks with my proven Starbourne Accelerator 4. Infrastructure built to scale — fast, modular, GPU-ready 5. Clear communication, async-first workflows, and sprint-based delivery 📞 NEXT STEP Click “Invite to Job” or book a quick 15-minute discovery call. Let’s build and launch your AI solution — fast, scalable, and production-ready.