Hire the Best AI Reliability Engineers

I’m an AI Automation Engineer focused on one outcome: building systems that run your operations without constant human involvement. Most clients come to me with the same problem. Their team is stuck spending 15–25 hours every week on repetitive tasks that shouldn’t require manual effort. Copy-pasting data between tools, chasing follow-ups, updating reports across multiple sheets all of it slows down growth. My approach is simple: identify the bottleneck that’s actually costing you time or revenue, and eliminate it with a scalable automation system. If there’s no clear ROI (time saved, errors reduced, revenue increased), I don’t build it. 𝗪𝗛𝗔𝗧 𝗜 𝗕𝗨𝗜𝗟𝗗 Most systems I design combine multiple layers of automation and AI. ◆ 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 & 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 I connect your entire stack into a unified system. CRMs, databases, APIs, and internal tools all working together without manual handoffs. From CRM restructuring (HubSpot, Salesforce, Pipedrive) to multi-platform workflows across 5–10+ tools, everything runs seamlessly. Reporting becomes real-time not something your team updates once a week. ◆ 𝗔𝗜 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 & 𝗔𝗴𝗲𝗻𝘁𝘀 I integrate AI where it actually adds value. Not hype real use cases. Lead qualification agents that score and route prospects instantly. RAG-based systems that allow AI to pull from your company’s actual data. Automated document processing for contracts, invoices, and support workflows. ◆ 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 & 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 I build the backend systems that let you scale without increasing headcount at the same rate. From onboarding pipelines to full operational audits, I design infrastructure that supports growth without operational chaos. 𝗧𝗢𝗢𝗟𝗞𝗜𝗧 I don’t chase trends I choose tools based on what your system actually needs. ◆ Automation: n8n (self-hosted & cloud), Make, Zapier, Workato ◆ AI & LLMs: OpenAI (GPT-5, Assistants API), Claude, Gemini ◆ AI Systems: RAG pipelines, AI agents, structured prompt workflows ◆ Voice AI: Retell, Vapi, ElevenLabs ◆ CRMs: HubSpot, Salesforce, Pipedrive, GoHighLevel, Monday ◆ Data & Ops: Airtable, Notion, ClickUp, BigQuery ◆ Dev: APIs (REST/GraphQL), Webhooks, JavaScript, Python 𝗪𝗛𝗬 𝗪𝗢𝗥𝗞 𝗪𝗜𝗧𝗛 𝗠𝗘 I focus on systems not isolated tasks. You’re not hiring me to set up a few automations. You’re hiring me to analyze your operations, identify the highest-impact opportunity, and build something that actually lasts. Everything I create is structured, documented, and built to scale. Most projects start with one automation. Then naturally expand because once the first bottleneck is removed, the next one becomes obvious. Send me a message with your current workflow. I’ll tell you honestly whether it’s worth automating and what the system would look like.

I am a creative engineer with well developed problem solving skills and analytical abilities. I build AI agents that survive production, systems that stay accurate under real users, real edge cases, and real compliance rules. I work with companies and individual founders to design agents that hold up once they're live, and I bring enterprise-grade engineering to teams of every size. Most AI features demo well and fall apart in month two. My work is the opposite problem: making the thing reliable enough that a business can put its name on the output. I've built and shipped generative AI applications at enterprise scale and led AI work at that level, so the architecture, observability, and review practices that large organizations rely on come standard, whether you're a 200-person company or one person with a product. AI Agents & Copilots Designing agents that operate inside real products: tool calling, multi-step workflows, structured outputs, streaming, generative UI, and copilot interfaces embedded in an existing editor or app rather than bolted on as a chat window. Reliability & Evaluation Langfuse observability and tracing, PostHog product analytics, benchmark datasets and graded eval suites, regression testing across model and prompt changes, cost and latency optimization, human-in-the-loop review, and hard constraints for regulated or compliance-bound output. LLM Engineering OpenAI API, Anthropic/Claude API, Azure OpenAI Service, model-agnostic architecture with provider fallback, prompt engineering and few-shot design, RAG and retrieval pipelines, citation and grounding systems, fine-tuning where it earns its cost, Vercel AI SDK, CopilotKit. Backend & Systems Python (FastAPI, Django), Node.js, RESTful API design and integration, Convex, Postgres, async and streaming architectures, API testing and integration testing. Frontend React, Nextjs, TypeScript, JavaScript, HTML/CSS, responsive interfaces, real-time and streaming UI. Cloud & Infrastructure AWS, Google Cloud Platform, Azure, scalable deployment for AI workloads. Also SEO, GEO and AEO for AI-crawler visibility, Conversational AI and chatbot development (OpenAI, WhatsApp, Discord), NLP, speech recognition and synthesis via Whisper, automation tooling (n8n, Make .com, Zapier, Twilio).

This is Smeet, an AI & GenAI Engineer and Technical Lead with 7+ years of software engineering experience, specializing in Artificial Intelligence, Generative AI Software, Large Language Models (LLMs), AI Agent Development, Retrieval Augmented Generation (RAG), OpenAI API, and LangChain. I build production-ready AI agents, RAG systems, LLM applications, AI chatbots, workflow automation, and AI-powered SaaS products, combined with Full-Stack Development and Web Development using Python, React, Next.js, Node.js, NestJS, and PostgreSQL. I build AI-powered automation, agent workflows, and document intelligence systems that connect LLMs with real business processes, APIs, and SaaS platforms. My focus is turning AI from a standalone feature into a reliable workflow—whether that means automating document processing, connecting AI to business systems, building agentic workflows, or integrating LLMs into existing applications. If you have an AI workflow that needs to move from idea/prototype to a working production system, I can help design the architecture and build the implementation. Types of Projects I have Delivered 1) AI & GenAI Applications: AI agents, RAG systems, LLM applications, OpenAI integrations, AI chatbots, and workflow automation. 2) AI-Powered SaaS: Scalable SaaS platforms with AI capabilities, subscription workflows, and production deployment. 3) Full-Stack Web Applications: React, Next.js, Node.js, NestJS, Python, and PostgreSQL. 4) Enterprise Applications: Production applications serving 22,000+ users within enterprise environments. 5) Real-Time Applications: WebSocket-based communication and real-time systems. 6) Subscription & Payment Systems: Subscription billing and automated payment workflows. 7) AWS Cloud Solutions: EC2, S3, CloudFront, Lambda, RDS, CI/CD, serverless architecture, and cloud deployment. 8) Cloud & DevOps: Infrastructure, deployment automation, CI/CD pipelines, and production environments. 9) Blockchain & Web3: Blockchain integrations, Solidity, smart contracts, and dApps. How I Handle Projects 1. Understand:- Clarify your business goals, requirements, users, and technical challenges. 2. Architect:- Define the system architecture, technology stack, APIs, database, AI approach, and cloud infrastructure. 3. Build:- Develop clean, maintainable, scalable solutions using proven engineering practices. 4. Test & Secure:- Validate functionality, integrations, performance, security, and production readiness. 5. Deploy:- Implement CI/CD, cloud infrastructure, deployment, and production configuration. 6. Communicate:- Work with clear milestones, transparent timelines, regular updates, and responsive communication. 7. Optimize & Support:- Improve scalability, performance, reliability, and support post-launch requirements. My Professional Experience: a) Technical Lead: AI & Full-Stack Development | AI Software Development Company. Lead architecture and delivery while managing a 15-member engineering team across frontend, backend, and DevOps. b) Full Stack Engineer (Contract – Remote) | Johnson & Johnson. Delivered enterprise features for applications serving 22,000+ users under strict security, compliance, code review, and CI/CD standards. c) Full Stack Developer | AI Software Development Company Built and shipped 12+ client-facing applications, including SaaS platforms, subscription billing, automated payments, and real-time WebSocket systems. Cloud & Technical Expertise AI/GenAI: Artificial Intelligence, Generative AI Software, LLMs, AI Agents, RAG, OpenAI API, LangChain, AI Chatbots, Workflow Automation Full-Stack: Python, React, Next.js, Node.js, NestJS, PostgreSQL, Web Development Cloud & DevOps: AWS, Amazon EC2, S3, CloudFront, Lambda, RDS, Azure, GCP, CI/CD Pipelines, Serverless Architecture, Cloud Deployment Blockchain/Web3: Blockchain, Solidity, Smart Contracts, dApps, Blockchain Integrations Why Clients Work With Me: i) 7+ years of software engineering experience ii) 15-member engineering team leadership iii) 12+ applications delivered iv) Enterprise experience supporting 22,000+ users v) 50% faster delivery through standardized architecture and AI-assisted development vi) Strong focus on security, scalability, performance, and production readiness vii) Clear communication and end-to-end ownership My Working Style I believe successful projects require more than good code. I focus on understanding the business objective, designing the right architecture, executing with clear milestones, communicating consistently, and taking ownership through production. Have an AI idea, existing product, or technical challenge? Send me your requirements, and let’s turn it into a scalable, production-ready solution.

I build AI systems that make it to production, not demos that die in a notebook. I'm Jürgen, an AI/ML engineer with 15+ years designing, deploying, and maintaining machine learning and LLM systems in live, revenue-critical environments. I've delivered 200+ data science and AI projects across 13 international markets, for clients ranging from the World Bank and JSE Top 40 enterprises to government agencies and early-stage startups. WHAT I DO • RAG & AI Agent Systems: Retrieval-augmented pipelines and autonomous agents that don't just answer questions but take action: process documents, make decisions, call APIs, and trigger downstream workflows. I work with GPT-4 and Claude, vector databases including ChromaDB and Pinecone, Cohere reranking, and agent orchestration frameworks. My focus is on the hard part, meaning retrieval quality, evaluation, and reliability at scale, not just the happy path. • Production ML Pipelines: End-to-end systems in PyTorch, TensorFlow, and scikit-learn, covering feature engineering, training, validation, deployment, and monitoring. One example: a proprietary credit risk model I built and operationalized handles 150,000+ API calls per year in production. • Backend & MLOps: Python services built with FastAPI, Flask, and Django, deployed on AWS using Lambda, ECS, and CloudFormation, containerized with Docker and shipped through automated CI/CD. Every model I deploy comes with monitoring, drift detection, and alerting. I own my deployments. I don't hand a pickle file to someone else and hope. • Predictive Analytics & Risk Modeling: Credit scoring, fraud detection, behavioral analytics, time-series forecasting, and A/B testing frameworks, built on PostgreSQL, Snowflake, and Databricks. Where regulation applies, I implement SHAP-based explainability so model decisions can be defended to auditors and regulators. WHY CLIENTS KEEP ME ON • I've seen models fail in production. After 15 years, I design for drift, edge cases, and the day your data source silently changes format. • PhD-level rigor where it counts. Statistical modeling, causal inference, and experimental design, backed by 30+ peer-reviewed publications and $1.5M+ in secured research funding. That means I can tell you when your data can't support the question you're asking, before you spend three months finding out. • I explain things clearly. Technical decisions, trade-offs, and risks in plain language your stakeholders can act on. TYPICAL ENGAGEMENTS LLM agent development, RAG system design and optimization, ML model development and deployment, MLOps and pipeline architecture, technical due diligence on existing AI systems, and fractional AI lead work for teams without in-house ML expertise. If you have an AI project that needs to actually work in production, or an existing system that isn't performing, send me the details and I'll tell you honestly whether I'm the right fit.

Your AI chatbot makes things up. It gives answers that sound right but aren't, pulls the wrong document, or can't show where an answer came from. I audit AI systems like that, find the real cause, and fix it. I build RAG systems and AI agents in Python. Most AI projects don't fail at the demo. They fail the week after, when a real user asks a real question and gets a confident, wrong answer. ── RECENT RESULTS ── • Multi-agent client support and booking system for a beauty and aesthetics group in Prague. It classifies incoming WhatsApp, Instagram and email messages by intent and urgency, answers from RAG over their treatment menu, pricing and aftercare documents, and escalates medical concerns and high-value bookings to a human with a clean summarised handoff. 97% accuracy, designed and shipped in 7 days. The client expanded the scope four times over during the project. 5.0 stars. • Reliability audit and rebuild of an existing chatbot. I traced the real failure modes rather than guessing at them, then reworked chunking, retrieval and grounding around what I found. Answer accuracy went from 69% to 92%. ── WHAT I FIX ── "Our assistant invents answers." → Grounded RAG with source citations. Every answer links back to the document it came from. "It can't find the right document." → Retrieval tuning: chunking strategy, hybrid search, re-ranking, metadata filters. Measured against your real questions, not guessed at. "It works on 10 documents, not 10,000." → Production ingestion over PDF, DOCX, Confluence, S3, with incremental re-indexing instead of full rebuilds. "We can't tell whether a change made it better." → Eval harnesses that score accuracy, groundedness and latency on every deploy. "It answers, but it doesn't do anything." → Agents wired into your CRM, internal APIs and databases, with logging, guardrails and human-in-the-loop approval on anything that matters. ── STACK ── Python · LangChain / LangGraph · OpenAI, Anthropic Claude, open-weight models pgvector · Pinecone · hybrid search · re-ranking · eval harnesses FastAPI · PostgreSQL · Docker · AWS · n8n · REST and webhook integrations ── WHY ME SPECIFICALLY ── I'm a Staff Software Engineer at Outreach, a US enterprise SaaS company, where I ship AI features that run against real customer data at scale. Before that I was Director of Software Development at FCR Immobilien AG, leading 24 engineers across two countries and building a high-load microservices CRM. I also cut that company's AWS bill from $16,000 to $6,000 a month — which matters more on an AI project than it sounds, because runaway inference and infrastructure spend is where these systems quietly go wrong. The hard part of AI work isn't the prompt. It's everything around it: data pipelines, observability, cost, failure modes, and what your system does when the model is wrong. I've spent ten years on that half of the job. You work with me directly. No agency, no account manager, no handoff to a junior. ── HOW WE START ── Most clients begin with a fixed-price AI Reliability Audit. I take your real failure cases, review the pipeline, and hand back a written diagnosis with a prioritised fix list — yours to keep whether or not we work together. If you'd rather go straight to building, that works too. ── GOOD FIT ── ✓ You have real data, real users, and a prototype that isn't good enough yet ✓ B2B or enterprise, where a wrong answer costs something ✗ "Build me an AI app" with no defined use case ✗ Lowest bid wins Message me with what's breaking. I'll tell you in plain language what I think is causing it, and roughly what it takes to fix — before you spend anything.

I build production RAG systems, LLM applications, and AI agents that answer accurately, stay on-topic, and keep working after launch. If your AI is hallucinating, retrieving the wrong information, responding too slowly, becoming expensive at scale, or remaining stuck in prototype mode, I can help you make it production-ready. Most AI projects don’t fail because of the model. They fail because of unreliable data, weak retrieval, missing evaluation, uncontrolled hallucinations, poor latency, and costs that increase unexpectedly at scale. Over the past six years across Upwork and off-platform product engagements I’ve helped 30+ teams turn AI concepts into systems used in real operational environments. SELECTED RESULTS → Built a RAG assistant across a 10,000+ document knowledge base, achieving 95%+ evaluated answer accuracy and supporting approximately 500 daily users. → Delivered a clinical documentation product that reduced documentation time by up to 70% and improved transcription accuracy by 30%. → Developed and tested computer-vision models for real-time unsafe-driving detection, including the evaluation and monitoring needed for production use. WHAT I CAN HELP YOU WITH → Production RAG systems → LLM applications, copilots and customer-facing assistants → AI agents with tools, memory, validation and human review → Retrieval evaluation and hallucination testing → Hybrid search, reranking and vector database architecture → AI workflow automation and API integrations → Healthcare and privacy-sensitive AI applications → Rescue and stabilization of incomplete AI products → AI performance, latency and token-cost optimization → Production deployment, monitoring and maintenance MY RELIABILITY PROCESS 1. Audit I examine your data, users, current architecture, failure cases and success criteria before recommending a solution. 2. Ground I design retrieval, prompts, permissions, validation and guardrails so responses remain connected to trusted information. 3. Evaluate I build repeatable tests for retrieval quality, answer accuracy, hallucinations, edge cases, latency and cost. 4. Deploy I implement monitoring, fallbacks, access controls and production safeguards so the system continues performing after launch. CORE TECHNOLOGIES Python · OpenAI · Anthropic · LangChain · LlamaIndex · FastAPI · PostgreSQL · pgvector · Pinecone · Weaviate · Chroma · Redis · Docker · AWS · GCP · Azure A GOOD FIT IF YOU: → Need to take an AI prototype into production → Have a RAG system producing inaccurate answers → Need an AI agent that can safely perform multi-step work → Want to reduce LLM latency or operating cost → Need an independent technical assessment before investing further → Have inherited a partially completed AI application that needs rescuing Not ready for a complete build? We can begin with a fixed-scope AI Reliability and Architecture Audit. Send me a short description of what you are building or what is currently failing and I’ll respond with the first technical questions I would investigate.