Hire the Best Vertex AI Specialists

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

I build AI systems that actually make it to production, not demos that die after the pitch meeting. Over the past few years I've shipped autonomous AI agents with LangChain/LangGraph and n8n, RAG pipelines, and conversational AI Agents running live on WhatsApp, Instagram, and Line. I also build full AI-native products: web apps, custom CRMs, and analytics dashboards designed around LLMs from day one instead of having AI bolted on later. Everything is written in TypeScript/Node.js or Python and deployed on GCP and AWS. One recent build was a contract analysis pipeline for the music industry. Specialized agents for Producer, Mixer, Featured Artist, and Publishing deals run under a LangGraph Supervisor on Cloud Run. Document review that used to take hours per deal now takes minutes. Things clients hire me for: - AI agents and multi-agent systems, using LangGraph architectures for workflows too complex for a single prompt - AI-native web apps and SaaS. I built and shipped ReserveNova, an AI booking platform for hospitality, end to end - Chatbots and conversational AI with real memory, integrated into CRMs like GoHighLevel and Follow Up Boss - RAG pipelines and semantic search with Pinecone, ChromaDB, and PGVector at production scale - Workflow automation with n8n and LangChain, wired into CRMs, ERPs, and third-party APIs - Document intelligence and AI dashboards: contract analysis, data extraction, and real-time LLM-powered KPIs How I work is simple. I scope the architecture before writing a line of code, get you a working prototype within 48 to 72 hours so you can validate the direction early, then build toward production with proper error handling, observability, and cloud infrastructure. You'll always know what's being built and why. My background runs deeper than the LLM wave. I spent years doing hands-on natural language processing work, including text classification, entity extraction, and semantic search with transformers and spaCy, well before agents became the trend. That's why my RAG pipelines and chatbots handle messy real-world language instead of breaking on it. Stack, for those who want the details: TypeScript/Node.js, Python, React/Next.js, LangChain, LangGraph, LlamaIndex, CrewAI, GPT-4o, Claude, Gemini, Pinecone, PGVector, ChromaDB, n8n, HuggingFace Transformers, spaCy, GCP Cloud Run, AWS Lambda, Docker. I keep my project load small so each build gets real attention. If you're building an AI product from scratch, adding agents or RAG to an existing app, or drowning in a document-heavy workflow, send an invite and tell me what you're trying to build. I'll tell you honestly whether it's a fit.

𝗜 𝗯𝘂𝗶𝗹𝗱 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗔𝗜 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 — 𝗻𝗼𝘁 𝗽𝗿𝗼𝘁𝗼𝘁𝘆𝗽𝗲𝘀. Most AI projects look great in a demo and break when they meet real work. The agent routes to the wrong tool. The RAG retrieves the wrong chunks. The voice latency kills the conversation. The pipeline runs in a notebook but never makes it to production. That's where I come in. In 2 months at a Dubai tech company, I built and deployed 6 production AI services from zero infrastructure. Voice assistants, document tools, RAG pipelines, agent workflows — all live, all on AWS, all monitored with LangSmith. ─────────────────────────── WHAT I'VE SHIPPED ─────────────────────────── ◆ Voice assistant with 7-agent architecture — real-time speech (Deepgram + LiveKit), Text-to-SQL, RAG document Q&A, multi-agent routing via LangGraph ◆ AI Status Reports — optimized 17s → 2.1s, beat the 5-second requirement by 58% ◆ Document Classifier — bulk upload with Gemini 2.0 Flash. $0.0002/doc, 100% accuracy in production testing ◆ COSTRA — 5-agent BOQ extraction (internal tool) — vision models (Claude 4.5 Sonnet, Gemini 3 Pro) extracting data from construction PDFs, exports to Excel/JSON ◆ Medical RAG (graduation research, distinction with honors) — F1: 79.3 / EM: 69.4 on LitQA v2 with hybrid retrieval, cross-encoder reranking, context compression ─────────────────────────── WHAT I BUILD FOR YOU ─────────────────────────── ◆ Multi-Agent Systems — LangGraph, LangChain, tool routing, structured outputs, conditional flows ◆ Voice AI Agents — phone calls (Telnyx), browser-based (LiveKit), real-time STT/TTS (Deepgram, ElevenLabs), Silero VAD ◆ RAG Pipelines — over your documents, databases, and APIs. Hybrid retrieval, reranking, evaluation. ◆ LLM Apps & APIs — Claude, GPT-4, Gemini, Groq, or self-hosted (vLLM, Ollama) ◆ Production Deployment — FastAPI → Docker → AWS (Lambda, EC2, Bedrock) with CI/CD, Caddy reverse proxy, LangSmith monitoring ─────────────────────────── TECH STACK ─────────────────────────── Languages & Frameworks — Python, FastAPI, Flask, Next.js, React Agents & Orchestration — LangGraph, LangChain, multi-agent systems, tool calling RAG & Retrieval — Hybrid retrieval (BM25 + dense), cross-encoder reranking, context compression, chunking strategies Voice AI — Deepgram, LiveKit, Telnyx, ElevenLabs, Silero VAD, Gemini Live API LLMs — Claude, GPT-4, Gemini, Groq, AWS Bedrock, vLLM, Ollama Vector Stores — FAISS, Pinecone, Qdrant, Milvus, Chroma, pgvector Cloud & Infra — AWS (EC2, Lambda, Bedrock, API Gateway, S3, Amplify), Docker, Coolify, CI/CD Observability — LangSmith, Langfuse Backend — PostgreSQL, Supabase, Neo4j AuraDB Automation — n8n, REST APIs, webhooks ─────────────────────────── AWS Certified Cloud Practitioner. Tell me what you're trying to build. I'll tell you honestly if it's the right approach and how I'd build it.

🚀 Top-Rated Plus AI/ML Engineer | NLP | LLMs | GenAI | FastAPI | RAG | LangChain | AI Agents | Finetuning | Azure & GCP | 40+ Projects Delivered I help startups and enterprises turn AI ideas into real, revenue-driving products—specialising in LLMs, AI agent orchestration, and end-to-end automation systems. With 5+ years of deep experience and over 40+ successful projects, I bring both technical execution and product thinking to every engagement. ✅ In Top 1% AI/NLP Specialist on Upwork with 100% Job Success ✅ Built MVPs and deployed AI systems for funded startups ✅ Hands-on with OpenAI, Claude, Mistral, LLaMA, Azure OpenAI, RAG pipelines & vector DBs ✅ Strong background in classical ML, analytics, and backend systems (FastAPI, GCP, Azure) 🧠 Core Strengths - LLMs & GenAI: GPT-4, Claude, LLaMA, fine-tuning with PEFT (LoRA, QLoRA), prompt optimization, embedding generation - AI Agent Architectures: LangChain, LlamaIndex, AutoGen, agent tool-use, function calling, workflow agents - Retrieval-Augmented Generation (RAG): Multi-source RAG systems, document chunking, semantic search with Weaviate, Pinecone, FAISS - FastAPI & Backend APIs: Built production-ready AI APIs with user auth, logging, streaming, and OpenAI integrations - Classical ML & Data Science: Regression, classification, clustering, scikit-learn, feature engineering, pandas, SQL, Spark - Cloud & MLOps: Azure (AI Studio, Blob, Functions), GCP (Vertex AI, Cloud Run), Docker, CI/CD pipelines - Vector DBs & Data Stacks: Weaviate, Pinecone, PostgreSQL, MongoDB, BigQuery, Cosmos DB 💼 Featured Project – REVOPS 360 (From Idea to MVP Startup) ✅ Built the entire GenAI system for a B2B RevOps platform—including lead generation agents, campaign creators, pitch generators, LinkedIn assistants, and dashboard integrations. ✅ Delivered in sprint-based execution from zero to MVP, with 20+ modular tools, FastAPI backend, and LLM routing logic based on brand tone, channel type, and conversion goals. ✅ Supported by an Australian startup, now entering the venture capital phase. 🌟 Other Highlights 🏆 AI Customer Support Agent: Built an OpenAI-powered function-calling bot that reads and responds to emails, integrated with PHP functions and dynamic memory. 🏆 Real Estate AI Engine: Designed ML + LLM workflows to predict future prices, regional growth zones, and apartment valuation insights. 🏆 Resume Matcher (NLP): Built transformer-based shortlisting system reducing hiring time by 70%. 🏆 Crypto Bot & Data Dashboards: Applied classic ML to real-time time series with smart visualization via Dash and Streamlit. 🔥 Why Clients Hire Me 🎯 Startup-Ready Execution: I’ve taken clients from idea to MVP and production—owning backend, ML, and cloud. 📊 AI That’s Aligned with Business: I don't just build fancy models. I deliver outcomes that save time, convert users, and scale. 🤖 Deep Technical Stack: From PEFT finetuning and RAG optimization to MLOps and deployment, I’ve handled the entire pipeline. 🤝 Great Communication, Long-Term Focus: I build trust fast and stick around to improve what we ship. Industry Experience 4 Years: 🤖 Generative AI Engineer / Data Scientist – Eriko AI (Sep 2023 – Present) Delivering advanced LLM systems, automation, and AI agents for marketing and enterprise use cases. 🧠 Machine Learning Engineer – DFKI (Jan 2024 – May 2024) Working on applied ML research and real-world GenAI solutions at one of Europe's leading AI research centres. 🏆 Top Rated Plus Freelancer – Upwork (Apr 2022 – Present) Completed 25+ AI/ML/NLP projects with full-stack delivery for startups and enterprise clients, maintaining 100% client satisfaction. 🌍 Machine Learning Engineer – Ezy-AI (Jan 2023 – Sep 2023) Developed multi-language chatbots, voice-based automation, and intelligent scraping systems deployed for global clients. 📊 Research Assistant – NYU Tandon School of Engineering (Aug 2022 – Dec 2022) Built large-scale NLP systems and prediction models analysing millions of Amazon products using advanced ML pipelines. 📈 Associate Consultant – Systems Limited (Jul 2021 – Jun 2022) Contributed to enterprise ML/NLP projects, SQL pipelines, and dashboard systems for large-scale clients. 💡 Junior Consultant – Systems Limited (Jul 2022 – Aug 2022) Focused on NLP, ML model development, and performance optimisation for client-based projects. ---- ### OpenClaw | NanoClaw | NemoClaw | Claude Agent Bots Expert (12+ Projects) I set up OpenClaw, NanoClaw, and NemoClaw correctly — with hardened SKILL.md, sandboxed credentials, and optimised context. Most clients pay $100+ monthly because skills bloat tokens, secrets sit in markdown files, and memory drifts across sessions. I fix all three: cost down 40-70%, credentials vaulted via OneCLI, context compacted, channels properly wired across WhatsApp, Telegram, Discord, Slack, Signal, and iMessage. Let's transform your AI vision into a working product—scalable, efficient, and ROI-driven. 📬 Ready to start? Let’s chat!

Availability: Full-time freelancer, 𝟰𝟬+ hours/week, open to long-term collaborations. I’m a Full-Stack & AI Engineer with 10+ years of experience building web and mobile applications and 3+ years of specialized experience in AI and Large Language Models (LLMs). I design, develop, and deploy production-grade platforms, from scalable SaaS dashboards to AI-powered assistants, RAG systems, and voice agents. I work end-to-end: architecture → backend → frontend → cloud deployment, with a focus on clean code, maintainable systems, and high performance. Over the past few years, I’ve delivered solutions that integrate AI/LLM pipelines, vector search, real-time chat, and voice agents for enterprise and startup clients. 🤖 AI & LLM Expertise - MCP Server Development: Designing and integrating custom MCP servers for AI agents, enabling structured tool usage, external system integrations, database querying, and API orchestration. - Fine-Tuning: Persona creation, Q&A systems, and domain-specific models (medical, legal) using Mistral and Llama 3. - Synthetic Dataset Generation: Streamlining LLM training with high-quality datasets. - Evaluation Frameworks: Assessing LLM performance with custom metrics. - Cloud Deployment: Deploying LLMs on AWS and GCP. - AI Agents & Voice Bots: Proficient with LiveKit, Retail AI, OpenAI. - Open-Source Deployment: Expertise deploying models like vLLM on AWS/GCP/RunPod using SkyPilot. 🛠️ 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗧𝗼𝗼𝗹𝘀 & 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 ➜ LLM Tools: LangChain, Langsmith, Langfuse , Hugging Face, Transformers. ➜ Vector Databases: Chroma, FAISS, Pinecone, Qdrant , Opensearch ➜ AI Workflows: Flowise AI, LangFlow, StackAI. 🛠️ 𝗙𝘂𝗹𝗹 𝗦𝘁𝗮𝗰𝗸 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗘𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲 ➜ Languages & Frameworks: Python, Node.js, ReactJS. ➜ Database Management: MongoDB, MySQL, PostgreSQL , Supabase , FIrebase ➜ Frontend & Backend Integration: Seamlessly connecting APIs and user interfaces. 🌟 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗦𝗸𝗶𝗹𝗹𝘀 ➜ Open-Source LLMs: Proficiency in LLAMA 3, Mistral 7B, and Mixtral 8x7B. ➜ Prompt Engineering: Expertise in techniques like Chain of Thought, Few-shot Prompting, and Self-Reflection. ➜ Fast Inference: Implementing high-speed solutions with vLLM . 🌟 𝗪𝗵𝘆 𝗖𝗵𝗼𝗼𝘀𝗲 𝗠𝗲? With over 10 years of experience, I deliver scalable, cutting-edge solutions tailored to your project’s needs. Whether it's advanced AI models, MCP server development, LLM optimization, or full-stack development, I ensure top-notch results every time. Let’s collaborate to bring your ideas to life!

𝐀𝐈 𝐢𝐬 𝐨𝐧𝐥𝐲 𝐯𝐚𝐥𝐮𝐚𝐛𝐥𝐞 𝐰𝐡𝐞𝐧 𝐢𝐭 𝐫𝐮𝐧𝐬 𝐲𝐨𝐮𝐫 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 — 𝐧𝐨𝐭 𝐰𝐡𝐞𝐧 𝐢𝐭 𝐬𝐢𝐭𝐬 𝐢𝐧 𝐚 𝐝𝐞𝐦𝐨. I design and build 𝐞𝐧𝐝-𝐭𝐨-𝐞𝐧𝐝 ai systems that combine 𝐦𝐚𝐜𝐡𝐢𝐧𝐞 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠, 𝐚𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬 𝐚𝐠𝐞𝐧𝐭𝐬, 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧, and 𝐟𝐮𝐥𝐥-𝐬𝐭𝐚𝐜𝐤 𝐚𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 into one cohesive, production-ready ecosystem. 𝐅𝐫𝐨𝐦 𝐝𝐚𝐭𝐚 𝐢𝐧𝐠𝐞𝐬𝐭𝐢𝐨𝐧 → 𝐌𝐋 𝐦𝐨𝐝𝐞𝐥𝐢𝐧𝐠 → 𝐢𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐭 𝐚𝐠𝐞𝐧𝐭𝐬 → 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐞𝐝 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 → 𝐮𝐬𝐞𝐫-𝐟𝐚𝐜𝐢𝐧𝐠 𝐝𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝𝐬, 𝐈 𝐡𝐚𝐧𝐝𝐥𝐞 𝐭𝐡𝐞 𝐜𝐨𝐦𝐩𝐥𝐞𝐭𝐞 𝐥𝐢𝐟𝐞𝐜𝐲𝐜𝐥𝐞 — 𝐞𝐧𝐬𝐮𝐫𝐢𝐧𝐠 𝐲𝐨𝐮𝐫 𝐀𝐈 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧 𝐢𝐬 𝐫𝐞𝐥𝐢𝐚𝐛𝐥𝐞, 𝐬𝐜𝐚𝐥𝐚𝐛𝐥𝐞, 𝐚𝐧𝐝 𝐛𝐮𝐢𝐥𝐭 𝐟𝐨𝐫 𝐫𝐞𝐚𝐥-𝐰𝐨𝐫𝐥𝐝 𝐮𝐬𝐚𝐠𝐞. 🚀 𝐖𝐡𝐚𝐭 𝐈 𝐁𝐮𝐢𝐥𝐝 (𝐄𝐧𝐝-𝐭𝐨-𝐄𝐧𝐝) 🤖 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 & 𝐎𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧 I specialize in "Agentic" workflows where AI can use tools, remember context, and collaborate: Frameworks: LangGraph, CrewAI, and LangChain. Capabilities: Tool-calling, persistent memory, and multi-agent systems. Agentic Workflows: n8n-based logic for complex decision routing. Use Cases: Automated Lead Qualification, AI Support Resolution, and Operations Routing. 📊 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 & 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 I build custom ML pipelines that turn raw data into foresight: Forecasting: Predictive analytics for sales and market trends. Security: Anomaly and fraud detection systems. End-to-End MLOps: Data collection, feature engineering, model training, and monitoring. ⚙️ 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐭 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 I replace manual overhead with event-driven ecosystems: Platforms: Expertise in n8n, Make, and Zapier. Custom Logic: Deep Python-based automations for complex logic. Impact: Real-time reporting, automated sales pipelines, and AI-triggered operations. 🌐 𝐅𝐮𝐥𝐥-𝐒𝐭𝐚𝐜𝐤 𝐀𝐈 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 I provide a single point of accountability for your entire application: Backend: Production-grade FastAPI and Node JS APIs with RAG integration. Frontend: React dashboards and AI Copilot interfaces. Knowledge Bases: Vector databases like Pinecone and Weaviate for enterprise RAG. 🛠️ 𝐂𝐨𝐦𝐩𝐥𝐞𝐭𝐞 𝐓𝐞𝐜𝐡 𝐒𝐭𝐚𝐜𝐤 AI & Agents: LangGraph, CrewAI, LangChain, OpenAI, Claude, Gemini. ML Systems: Python, PyTorch, Scikit-learn, Hugging Face. Automation: n8n, Make, Zapier, Custom Python Scripting. Full-Stack: React, FastAPI, Node JS, PostgreSQL, Vector Databases. Infrastructure: AWS, GCP, Docker (Microservices-ready). 💡 𝐖𝐡𝐲 𝐂𝐥𝐢𝐞𝐧𝐭𝐬 𝐂𝐡𝐨𝐨𝐬𝐞 𝐌𝐞 Systems Thinking: I design interconnected AI ecosystems, not isolated features. Production-First: No fragile prototypes. Every system is built to be secure and scalable. Direct ROI: I focus on automation that pays for itself by reducing overhead and error. 𝐋𝐞𝐭’𝐬 𝐭𝐮𝐫𝐧 𝐲𝐨𝐮𝐫 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 𝐢𝐧𝐭𝐨 𝐢𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐭, 𝐚𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬 𝐬𝐲𝐬𝐭𝐞𝐦𝐬. 𝐌𝐞𝐬𝐬𝐚𝐠𝐞 𝐦𝐞 𝐭𝐨 𝐝𝐢𝐬𝐜𝐮𝐬𝐬 𝐡𝐨𝐰 𝐰𝐞 𝐜𝐚𝐧 𝐬𝐜𝐚𝐥𝐞 𝐲𝐨𝐮𝐫 𝐨𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬 𝐰𝐢𝐭𝐡 𝐀𝐈.