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Are you building an AI product that needs to actually work in production ; not just impress in a demo? I'm Qasim Jalil, a Vetted AI/ML Engineer and Full-Stack Developer in the top 1% on Upwork, with 8+ years of experience, 80+ projects delivered, I don't just integrate APIs but I architect, fine-tune, and deploy end-to-end AI systems that scale. 🧠 WHAT I SPECIALIZE IN: → AI Agents & Multi-Agent Systems I build autonomous agents that plan, reason, and execute using CrewAI, AutoGen, LangChain, and LlamaIndex that's connected to real tools, APIs, and databases. → RAG Pipelines & Knowledge Systems End-to-end Retrieval-Augmented Generation using Pinecone, ChromaDB, FAISS, Qdrant, and Weaviate. I make your private data queryable, accurate, and hallucination-resistant. → LLM Fine-Tuning Custom model training with LoRA, QLoRA, RLHF, DPO, and PEFT on LLaMA 3, Mistral 7B, Mixtral 8×7B, Falcon, and Gemma using Unsloth, Axolotl, and HuggingFace AutoTrain. → Voice AI Agents Real-time voice pipelines built with Deepgram, Amazon Polly, Azure AI Speech, and Riverside SDK — bots that listen, understand context, and respond naturally. → LLM Deployment & Inference Optimization Production-grade deployments on AWS SageMaker, RunPod, and GCP using vLLM, TGI, and TensorRT-LLM — optimized for speed, cost, and scale. → Full-Stack AI Applications From React/Next.js frontends to FastAPI/Node.js backends — I deliver complete, production-ready AI products, not just model wrappers. ⚙️ CORE TECH STACK: LLM APIs: OpenAI GPT-4o · Claude (Anthropic) · AWS Bedrock · Ollama Agents: LangChain · LlamaIndex · CrewAI · AutoGen Voice: Deepgram · Amazon Polly · Azure Speech · Rasa AI Vector DBs: Pinecone · ChromaDB · FAISS · Qdrant · Weaviate Fine-Tuning: LoRA · QLoRA · RLHF · Unsloth · Axolotl · HuggingFace Inference: vLLM · TGI · TensorRT-LLM Backend: Python · FastAPI · Node.js · Django · Flask · NestJS Frontend: React · Next.js · TypeScript · Tailwind CSS · Vue.js Cloud & Infra: AWS (EC2, S3, Lambda, SageMaker) · GCP · Docker · Kubernetes · Redis ✅ WHY CLIENTS KEEP COMING BACK: 🔴 Top 1% Vetted Expert 🔴 100% Job Success Score across 80+ completed projects 🔴 AWS Certified & Python Certified — I understand infrastructure, not just model APIs 🔴 I ship production systems — scalable, documented, and maintainable 🔴 50 hrs/week available — fully committed, fast communicator, timezone-flexible 📌 RECENT PROJECTS: → Multi-agent customer support bot — CrewAI + GPT-4o + Pinecone + FastAPI → Real-time healthcare voice assistant — Deepgram + LLaMA 3 + AWS Lambda → Legal document fine-tuning — Mistral 7B with QLoRA (4-bit) on SageMaker → Internal knowledge base RAG system for a 500-person SaaS company → LLM evaluation framework + synthetic dataset generation pipeline on AWS 📩 LET'S TALK: Describe your project and I'll respond within a few hours. I offer a free 30-minute scoping call before any contract because the right fit matters more than a quick hire.

100+ AI agents shipped · 10+ voice AI products · Top Rated Plus · 100% JSS. I was building conversational AI before LLMs made it easy. Not just an AI Engineer — I scope like a consultant, plan like a product manager, and ship like a full-stack AI Engineer. 5+ years in chat and voice, now LLM-native: RAG, multi-agent systems (LangGraph, MCP), production voice agents (Vapi, LiveKit, FastRTC). I architect and ship complete systems, not prototypes. ___________________________ 👥 WHO I WORK WITH 👉 Founders who need their first AI product built right the first time — not a demo that falls apart in front of real users. 👉 Startups scaling a voice agent or RAG system from MVP to real production traffic. 👉 Teams adding AI to what they already run — phone agents, document search, internal copilots — without breaking what works. ___________________________ ⭐ WHAT MAKES ME DIFFERENT ✅ Conversational AI since before LLMs 5+ years building chat and voice systems — so I know what actually breaks in production: turn-taking, escalation, latency, context. Not just how to call an API. ✅ Consultant + PM + engineer in one I scope the problem, decide what's worth building, then ship it. No three hires, no handoffs, nothing lost in translation. ✅ End-to-end ownership Working systems in production — with docs, support, and a plan to scale. Never half-built prototypes. ✅ Top Rated Plus · 100% Job Success The track record speaks for itself. ___________________________ 🛠️ WHAT I BUILD → AI Products & MVPs RAG systems, multi-agent workflows, AI copilots, and document intelligence — idea to working product, not a demo. → Voice AI & Conversational Systems Production voice agents and phone systems, from IVR to complex multi-agent voice. Vapi, LiveKit, FastRTC, Twilio. → Full-Stack AI Apps The whole product, not just the model — Next.js + FastAPI + Convex/Postgres, built to run in production. → Workflow Automation Connecting AI to the tools you already use — FlowXO, n8n, Make, Zapier — so it fits your process instead of replacing it. ___________________________ 📦 WHAT I'VE DELIVERED A few systems I've shipped to production: 1️⃣ Voice AI for Healthcare — ThriveLink (Roger) A multi-agent voice agent that replaced clunky web forms with natural conversation. 24k+ conversations handled, 35%+ of users prefer voice over typing, 80%+ return rate. (LangGraph · Vapi · Twilio · OpenAI) 2️⃣ Enterprise Knowledge RAG — Knobi A RAG platform over internal docs and wikis with hybrid search and cross-encoder re-ranking — accurate, cited answers. ~80% of routine questions deflected, 10k+ docs indexed. (LangChain · Qdrant · FastAPI · Next.js) 3️⃣ AI Legal Assistant — PanteraGPT (Antonio) A multi-agent LangGraph pipeline for contract review, clause extraction, and compliance Q&A. ~60% less manual review time, 3k+ documents processed. (LangGraph · OpenAI · Bedrock · PostgreSQL) 4️⃣ Coaching Bot Platform — Coachendo A chatbot trainer-and-loader that lets non-technical teams spin up and deploy coaching bots in minutes. Plus voice and RAG systems for Doña Justina, Shopperfix, PRMediaLabs and more. ___________________________ 🧭 HOW I WORK 1️⃣ Discovery — your problem, your users, your constraints. I tell you honestly if I'm the right fit. 2️⃣ Plan & Architecture — a clear plan, timeline, and milestones before any code. 3️⃣ Sprint Delivery — 2–4 week sprints with progress you see weekly. Working software, not status updates. 4️⃣ Launch & Support — production deployment, docs, and a support window. Optional retainer. ___________________________ ⚙️ TECH STACK AI / LLM — LangGraph, LangChain, MCP, OpenAI, Bedrock Voice AI — Vapi, LiveKit, FastRTC, Twilio Vector & Search — Qdrant, OpenSearch Backend & Product — Python, TypeScript, FastAPI, Next.js, React, Convex, MongoDB, PostgreSQL, Redis Cloud & Automation — AWS, Vercel, FlowXO, n8n, Make, Zapier ___________________________ 💬 NEXT STEP Four ways to work together — every project quoted up front, no surprise invoices. Figures are starting points: 1️⃣ Consultation · £120/hr — architecture reviews and direction before you build the wrong thing. 2️⃣ Scoped Build · £6k+/project (most common) — fixed scope, fixed quote: builds, integrations, POCs. 3️⃣ Fractional · £350/day — part-time AI engineering at a set weekly cadence. 4️⃣ Embedded · Let's talk — open to the right full-time or contract role, owning systems architecture to production. Send me your idea with a rough budget. Within 24 hours you'll get: - whether I can help (I say no to ~40% — a bad fit wastes both our time), - a rough scope and timeline, - and 2–3 clarifying questions. No sales pitch, no pressure — just a straight answer.

I build AI systems that survive contact with real users, real traffic, and real invoices — not demos that break in week two. Most automation work falls into two camps. There are no-code builders who can wire an n8n workflow but can't tell you what happens when the LLM returns malformed JSON at 2am. And there are cloud engineers who can deploy anything but have never sat with a business owner who just wants missed calls to stop costing them money. I work across both. WHAT I ACTUALLY BUILD ▸ n8n automation & AI agents — self-hosted (Docker, VPS, nginx, Let's Encrypt) or cloud. Multi-step workflows with proper error branches, retries and dead-letter handling. Telegram, Slack, WhatsApp, Gmail, Google Sheets, CRM and webhook integrations. ▸ AI voice agents — inbound and outbound. Retell AI + Twilio (UK numbers) + GoHighLevel. Missed-call-to-text recovery, appointment booking, qualification and routing. Full stack: number provisioning, prompt design, call transfer logic, CRM writeback. ▸ RAG systems that don't hallucinate — pgvector on Postgres, hybrid dense + BM25 retrieval, LangChain orchestration. My preferred pattern is two-stage: deterministic SQL or structured retrieval first, then grounded synthesis, so every answer traces back to a source row rather than to the model's imagination. I've published a peer-reviewed paper on exactly this architecture. ▸ Production LLM applications — FastAPI + Next.js, streamed responses, Clerk auth, Stripe subscription gating. Deployed to Vercel for speed or AWS for scale: Lambda, App Runner, ECR, S3, API Gateway, CloudFront, Route 53. Amazon Bedrock (Nova, Claude) where the client needs data to stay inside their AWS account. ▸ Infrastructure that isn't clicked together by hand — Terraform with isolated dev/test/prod workspaces, GitHub Actions CI/CD using OIDC (no long-lived AWS keys anywhere), CloudWatch dashboards for token spend and latency, budget alarms so nobody gets a surprise bill. ▸ MCP servers and multi-agent orchestration — tool-calling agents with human-in-the-loop checkpoints at the steps where a wrong answer is expensive. WHAT MAKES THE DIFFERENCE Anyone can get an LLM to produce something plausible. The engineering is in making it produce the *same* thing tomorrow, proving where the answer came from, and knowing what it costs per call. So I build with that from day one: structured outputs with schema validation, retrieval that carries citations, eval sets before prompt tuning, retry and fallback paths on every external call, and cost observability wired in rather than bolted on afterwards. I've written and published research on production AI architecture and audit-trail design, and I've shipped multi-tenant SaaS on this stack — Next.js BFF, FastAPI compute API, queued CPU/GPU workers on AWS Batch, usage-based billing. That's also why I'm comfortable on projects where the output gets checked by someone other than the person who ordered it: compliance reviews, scientific workflows, financial reporting, anything with an audit trail. HOW I WORK I scope before I build. You get an architecture note and a fixed number before I touch a node. I write down what the system will refuse to do, not just what it will do — that's usually the part that saves the project. Handover includes documentation and a walkthrough recording, because an automation only I can debug is a liability, not an asset. Based in the UK. Comfortable working async across US and EU hours. If you're not sure whether your problem is an n8n workflow, a proper agent, or just a better-written Zap — message me and I'll tell you straight, even if the answer is "you don't need to hire anyone for this."

I help improve AI systems by delivering high-quality data annotation, linguistic QA, and localisation support. With hands-on experience in AI model evaluation and multilingual data review, I specialise in ensuring accuracy, consistency, and clarity across complex datasets. I’ve worked on refining AI-generated outputs, identifying edge cases, and improving overall model performance through detailed analysis and structured annotation. Here’s what I can help you with: • AI data annotation (text, language, and prompt-based tasks) • Linguistic QA and localisation review (tone, intent, cultural accuracy) • Dataset quality assurance and guideline compliance • Identifying ambiguity and resolving edge cases • NLP-focused evaluation and chatbot improvement • Multilingual content review (English, Urdu, Punjabi) I also bring a strong background in regulatory analysis and client-facing roles, meaning I communicate clearly, meet deadlines, and handle complex information with precision. I’m comfortable working independently in remote environments and thrive on detail-oriented tasks that require accuracy and consistency. If you're looking for someone reliable who understands both language and AI systems — let’s work together.

🔷 Senior AI Developer with 7+ years of experience, passionate about building multi-agent architectures, RAG systems, and LLM optimization solutions 🔵 AI expert leading projects for BMW, WPP and for successful Start-Ups Yofi.ai (raised $ 1.5mln), MediKit.ua (largest telemedicine company in Ukraine, 100 000 + consultations made) What I do for clients: ✅ Complex multi-agent AI systems with MCP integration ✅ Advanced RAG architecture with custom embeddings ✅ Custom LLM fine-tuning (GPT, Claude, Llama, Mistral, Qwen) ✅ LLM pipeline optimization ($25,000/month => $9,000/month for BMW) ✅ AI voice agents with Text-to-Speech & Speech-to-Text (ElevenLabs, Whisper) ✅ Enterprise chatbots with context awareness ✅ AI workflow automation for business processes ✅ Scalable cloud deployment with auto-scaling infrastructure, containerization (Docker, Kubernetes), CI/CD pipelines, and production monitoring across AWS, Azure, and GCP Some of my recent projects: 🔷 AI Transcriber for Healthcare - SaaS platform processing 3,000+ calls/month with Whisper + GPT for auto-summarization and KPI tracking. 🔷 Mental Health Chatbot (Avocado AI) - Empathetic multi-LLM ensemble chatbot trained on 90 hours of therapy sessions with safety checks. 🔷 BMW Internal Knowledge System - RAG-powered platform for candidate screening, interview scoring, and internal knowledge assistant. 🔷 Swiss Legal Research Platform - Agent-based GenAI system searching 100K+ legal documents with CrewAI and LangGraph orchestration. 🔷 AI Project Management Platform (Genbai) - Jira-style tool with text-to-SQL and RAG using fine-tuned GPT, Llama and Mistral models. 🔷 Voice AI for Real Estate - Virtual receptionist with VAPI, Elevenlabs + GPT for call handling, booking, and searchable transcripts. 🔷 E-commerce Shopping Assistant - Chat-based RAG assistant for 100K+ products with recommendation system, live inventory integration, and analytics pipeline. 🔷 VeryBoat WhatsApp Booking Agent - Automated yacht charter bookings via WhatsApp with payment processing and calendar sync. 🔷 Flamingo Logistics Chatbot - MCP-based system translating natural language to database queries for parcel tracking. 🔷 AI Candidate Matching Engine - ETL pipeline with real-time vector indexing and RAG scoring for recruiting automation. Reviews: ⭐⭐⭐⭐⭐ 𝗝𝗮𝗱𝗲 𝗡𝗲𝘄𝗯𝗲𝗿𝗿𝘆, 𝗦𝗲𝗻𝗶𝗼𝗿 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗿 𝗮𝘁 𝗪𝗣𝗣 Mykola has been an incredible asset to our team at WPP. His expertise as an AI developer is not only profound but also practically applied to real-world problems. His work has significantly contributed to the success of our projects. Mykola is a true professional and a pleasure to work with. ⭐⭐⭐⭐⭐ 𝗥𝘂𝘀𝗹𝗮𝗻 𝗞𝗿𝗮𝘃𝗲𝘁𝘀, 𝗖𝗘𝗢 𝗼𝗳 𝗠𝗲𝗱𝗶𝗸𝗶𝘁 Working with Mykola as our AI developer has revolutionized our marketing strategies at Medikit. His insights have significantly enhanced our targeting accuracy and campaign effectiveness, leading to a notable increase in customer engagement and ROI. Beyond his technical prowess, Mykola's collaborative spirit and dedication to excellence have been key to reshaping our marketing landscape. Technical Stack: 🔹 LLMs & AI Models: OpenAI GPT, Anthropic Claude, Google Gemini, Llama, Deepseek, Qwen, Mistral 🔹 AI Frameworks: LangChain, LangGraph, CrewAI, Autogen, LlamaIndex, Haystack 🔹 ML/DL: PyTorch, TensorFlow, scikit-learn, Hugging Face Transformers 🔹 Programming: Python, JavaScript/TypeScript, Node.js 🔹 Full Stack: React.js, Next.js, FastAPI, Flask, REST APIs 🔹 Databases: PostgreSQL, MongoDB, Redis, ElasticSearch, Weaviate, Qdant, Pinecone, Faiss 🔹Cloud & DevOps: AWS (SageMaker, Lambda, EC2, SQS), Google Cloud, Azure, Docker, Kubernetes 🔹 Voice AI: ElevenLabs, Whisper AI, Speech-to-Text, Text-to-Speech, Twilio 🔹Video Generation: Runway API, Kling AI 🔹 Monitoring: LangSmith, Weights & Biases, Prometheus, Grafana, CloudWatch 🔹 Security & Compliance: GDPR, HIPAA, SOC 2, PCI-DSS Why Work With Me: 🔷 As an experienced AI developer, I dig deep to understand your business - both technically and strategically. I'll give you honest feedback on your project, identify challenges early, and architect scalable solutions. 🔵If you're looking for an AI developer with proven expertise and a track record of delivering real ROI, let's discuss your idea.

I am a highly analytical and detail-oriented linguistics specialist holding an MA from Edinburgh University. My extensive background encompasses, survey authoring, spoken language assessment, discourse analysis, AI-driven language evaluation, and a rich blend of educational and business experiences. This diverse expertise empowers me to adeptly handle projects involving customer surveying and comprehensive communication analysis. With a proven track record in employing structured scoring rubrics and analyzing linguistic nuances, I excel in ensuring objective and thorough language evaluation across various platforms, including video and text. My skills are not only suited for video-based assessments but also for developing and refining communication strategies, building customer and stakeholder surveys, enhancing AI language models, and crafting educational content that meets diverse learning needs. Adept at maintaining consistency, accuracy, and reliability, I am committed to delivering excellence in every project. Whether you are looking for an expert to refine language processing tools, evaluate communicative effectiveness, or build survey programs, I am equipped to provide high-quality, insightful contributions to your initiatives.