Hire the Best Artificial Intelligence Engineers
in Ukraine

Design and deploy AI pipelines that transform image, video, and audio data into reliable real-world applications, from computer vision analytics and 3D perception to audio (speech, music, sound) analysis and generative AI systems. My work focuses on production AI systems, not research prototypes. I help companies move from early feasibility studies and PoC development to scalable deployments running in real environments. Typical projects include: • computer vision pipelines for detection, tracking, and segmentation • real-time video analytics and edge AI deployments • 3D vision and spatial perception systems • speech processing and audio AI solutions • generative AI pipelines for image, video, and audio Computer Vision Systems Design and development of advanced computer vision pipelines for image and video analysis. Typical solutions include: • object detection and multi-object tracking • semantic and instance segmentation • pose estimation and motion analysis • OCR and document understanding • visual search and recognition systems These systems are used in industries such as manufacturing, sports analytics, retail, healthcare, and security. 3D Vision & Spatial AI Development of AI systems that understand spatial structure and depth. Experience includes: • structure-from-motion (SfM) • photogrammetry pipelines • depth estimation models • NeRF and neural rendering • point cloud processing and 3D reconstruction Applications include robotics, AR/VR, construction analytics, and digital twins. Edge AI & On-Device ML I specialize in deploying ML models on mobile and embedded devices where latency, memory, and power constraints are critical. Typical optimization techniques include: • model quantization and pruning • architecture optimization • real-time inference pipelines • deployment on mobile and embedded hardware Technologies include: TensorRT, TensorFlow Lite, CoreML, ONNX Runtime. Many deployed systems operate with 50–100 ms inference latency depending on hardware. Generative AI for Vision & Video Development of generative pipelines for media processing and synthetic data generation. Typical solutions include: • image and video generation pipelines • diffusion-based editing and enhancement • synthetic dataset generation for model training These tools help accelerate AI training and improve model robustness. Audio & Speech AI Development of AI systems for speech processing, audio analysis, and voice technologies. Examples include: • phoneme segmentation and pronunciation analysis • speech recognition pipelines • voice feature extraction and audio analytics • generative audio and music models These systems are used in: • language learning platforms • speech therapy tools • voice biometrics systems • music AI applications Technical Stack Frameworks & Models PyTorch, TensorFlow, OpenCV, Detectron2, MediaPipe, YOLO, DINO, SAM, CLIP Deployment TensorRT, TensorFlow Lite, CoreML, Docker, ONNX Runtime, FastAPI Programming Python, C, C++ 3D Vision NeRF, SLAM, Dust3r, point clouds Leadership & R&D I lead an R&D-focused AI team at It-Jim, an AI consulting company with 30+ engineers and 10+ PhDs specializing in: • Computer Vision • Generative AI • Audio & Speech AI • Edge AI systems We help companies solve technically challenging AI problems and build reliable production systems. If you are looking for experienced AI engineers to design, prototype, or deploy advanced machine learning solutions: feel free to reach out!

AI/ML engineer who cut a 6-hour manual review process to under 10 minutes using document AI and computer vision. 100% job success across 26 projects, $100K+ earned. I build AI systems that do real work. Results from deployed systems: Manufacturing Doc-AI — Computer vision (YOLOv8) + NLP on architectural blueprints for the largest US casework manufacturer. Estimation from 2-6 hours to under 10 min. 91% recall, 84% precision on production drawings. Detects cabinetry, reads MasterFormat specs, outputs structured estimates. AI Document Agent — Natural language search over 500MB+ PDF spec files. Answers with page + paragraph citations in under 30 seconds. Previously 30+ minutes per manual search. Multi-Agent Investment Research — LLM-based multi-agent system processing live financial data. Autonomous agent coordination with retrieval, analysis, and reporting pipelines. Backend & Long-Term Engagements — 4+ years with Xperi (DTS/TiVo), code running in BMW and Tesla. 5+ years with Internet Archive maintaining a 40,000-line Python platform powering worldwide book scanning. What I ship: LLM agents with tool calling, RAG over proprietary data, document AI (OCR + layout extraction), computer vision, and production Python backends. Weekly milestones with acceptance criteria. Handover includes runbooks, dashboards, and training.

I provide an AI Agent builder and AI Developer team that ships AI Workflow Automation and AI Chatbot products into production. We have delivered 200+ products across SaaS, marketplaces, AI tooling and internal platforms over 12+ years, and many of those clients have stayed with us for 3 - 10+ years. My job is to match each client with the right AI Agent builder and AI Developer engineers and stay accountable for every AI Agent builder engagement at production standard, not just a demo. Why hire my AI Agent builder team: - 200+ products shipped, now focused on AI Agent builder, AI Workflow Automation and AI Chatbot work - 12+ years of delivery, 100% JSS, Top Rated Plus - long-term clients who now bring us their AI agent and automation roadmap We work in two modes. AI-Accelerated: we run Claude, ChatGPT and Gemini across the cycle from PRD to test, which makes delivery roughly 40% faster. AI-Driven: my AI Agent builder team ships AI-powered products with full product discipline, with AI built in, not bolted on. Most of our work is AI Agent builder work, and the hard part is never the demo. A real AI Agent builder project is an ai agent that holds up in production: it handles edge cases, retries and fallbacks, keeps model cost under control, and passes safety evaluation before anything reaches a user. My AI Agent builder team treats an ai agent as full-stack software, with real state, memory, tool use, model routing, prompt versioning and the ai agent automation around it. We often take over an agentic ai developer hand-off when a solo builder cannot carry their ai agent to production. When a deterministic answer is the right one, we say so and put an Airtable workflow or a plain query behind the agent instead of another model call. AI Workflow Automation is the other half of most AI Agent builder briefs. My team builds AI Workflow Automation that removes manual steps end to end: an ai automation developer maps the process, the AI Agent builder layer runs it, and we connect it to the tools a business already uses through Zapier, n8n or a direct API. When the automation produces content, it plugs into copy generation, midjourney workflows, an ai video generator and ai artist assets. We treat AI Workflow Automation as production software, with monitoring, retries, cost guard-rails and a human in the loop wherever a wrong action is expensive. When conversation is the product, my AI Agent builder team works as an AI Chatbot and ai chatbot developer crew. We replace a brittle Botpress or gpt-3 era bot with a production AI Chatbot: an openai api developer wires the model layer, a generative ai developer shapes the outputs, and the result works as a real deal closer for sales and support rather than a toy widget. Every AI Chatbot we ship covers intent handling, escalation, analytics and the same safety evaluation every AI Agent builder product gets. As an AI Developer team we carry the whole system, not just the prompt. My AI Developer team turns python developer, machine learning and data scientist work into something shippable: an ai backend developer and an ai data engineer own the data layer, while an ai engineer and a machine learning engineer own the model layer. Clients come to my AI Developer team when their web developer cannot carry the AI layer, or when they need ai prompt engineering, an ai llm developer or a retrieval (RAG) build on top of an existing codebase. Clients hire my AI Agent builder team for: - an AI Agent builder team for production ai agent systems, from PRD to launch - AI Workflow Automation across the tools they already run, on Zapier, n8n or a direct API - an AI Chatbot, or an ai chatbot developer, that closes deals instead of deflecting them - an agentic AI developer for a LangChain or RAG build - python developer, machine learning, data scientist and machine learning engineer pairings - copy, midjourney, an ai video generator and ai artist tooling for generated content - an Airtable backend, or an Airbnb-style marketplace with AI on top - an ai app developer or full stack developer when the AI needs a real interface Recent work by the AI Agent builder team: - B2B luxury marketplace. React, Next.js, Laravel, Node.js, PostgreSQL, AWS, Stripe. 26K MAU, 75% MoM. - Identity-verification SaaS. React, Next.js, Laravel, Node.js, MySQL, AWS. API for dating, fintech, HR. - Web-safety platform. Next.js, Node.js, React Native, GCP. 170M+ req/week, 500 RPS, ~1M MAU. - Device-check SaaS. React, Next.js, Laravel, PostgreSQL, Cloudflare Workers, AWS. 4K+ DAU, 1M+ checks. - AI content platform. OpenAI API, Claude, Python, TensorFlow, Node.js, React. Manual review cut ~60%. Core stack: Node.js, TypeScript, Python, React, PostgreSQL, AWS, OpenAI, Claude, Gemini, LangChain, vector search, machine learning, deep learning, computer vision. Need an AI Agent builder team, AI Workflow Automation partner, AI Chatbot crew or AI Developer team that delivers on time and stays long-term - let's talk.

I help startups and B2B SaaS companies build, ship, and scale complete web applications powered by production-grade AI. Instead of hiring separate frontend, backend, and AI infrastructure engineers, I handle the entire product lifecycle — bridging the gap between robust, scalable full-stack architecture (NextJS, NodeJS, Python, AWS) and cutting-edge GenAI implementations (RAG pipelines, autonomous agents, and LLM orchestration). With 10+ years in full-stack development, $200K+ earned on Upwork, and a 100% Job Success Score (Top Rated Plus), I build with an ownership mindset to ensure your MVP hits the market fast and scales smoothly. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🌐 FULL-STACK & AI CAPABILITIES — WHAT I BUILD ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ → Full-Stack SaaS Platforms: Complete web applications with responsive UIs, secure multi-tenant auth, and complex business logic (React, NextJS, TypeScript). → AI Agents & Agentic Workflows: Multi-agent systems capable of autonomous execution, tool usage, and function calling (OpenAI, Claude, Gemini). → Production RAG Pipelines: Enterprise semantic search utilizing vector databases (Pinecone, pgvector, Qdrant) for accurate, private data querying. → Robust Backends & APIs: Highly performant, clean-code REST/GraphQL APIs and microservices (NodeJS, NestJS, Python, Express). → Automated Business Workflows: Intelligent document processing, automated lead scoring, and custom CRM/ERP AI integrations. INDUSTRIES I SERVE WITH AI + WEB SOLUTIONS: - Multifamily Real Estate — Custom web software layered over Yardi, AppFolio, Entrata, ResMan, RealPage, Apartments com - Hospitality / Hotels / Restaurants — AI booking flows, guest communication platforms, and review automation dashboards - MarTech / AdTech — Complete web analytics suites integrated with AdTech and MarTech API, and others like Meta, Google, TikTok, Yelp, and UberEats APIs ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🏆 RESULTS FROM RECENT PROJECTS ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ✔ AI Voice Agent SaaS Platform — Complete frontend UI/UX redesign + Supabase backend refactor, delivered in 90 days ($7,000 fixed price, 5.0 ★) ✔ Senior NodeJS SaaS Integration — 816 hours of self-driven backend delivery on schedule across 6 months ($32,633, 5.0 ★) ✔ AI-Powered Real Estate Onboarding — Built the web frontend and AI backend workflow that reduced onboarding drop-off and boosted conversions. ✔ Enterprise Software — Systems built by my team are used daily by Cushman & Wakefield (Fortune 500 real estate firm). ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ⚙️ TECH STACK ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ AI / LLM Integration: OpenAI API, Claude API, Gemini, LangChain, RAG, Vector Databases, Prompt Engineering, Function Calling, Structured Outputs Backend Development: Nodejs, NestJS, Python, Expressjs, Nextjs (Server Components) Frontend Development: React, TypeScript, Redux, Nextjs, Material UI, Tailwind CSS Mobile App Development: Flutter (Cross-platform, CI/CD with Codemagic) Cloud & DevOps: AWS (SQS, SNS, Lambda, RDS, ECS, ECR, CloudWatch, EventBridge, VPC, Route53, EC2), Terraform (IaC), Docker, Docker Swarm, Nginx Databases & Caching: PostgreSQL, MongoDB, MySQL, Redis, Pinecone, Qdrant, pgvector API & Enterprise Integrations (50+ types): REST / GraphQL / SOAP · OAuth2 / OIDC · Webhooks · ETL Pipelines · Payload Encryption ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🏢 ENTERPRISE & THIRD-PARTY INTEGRATIONS ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Property Management Systems (PMS): Yardi Voyager · AppFolio · Entrata · ResMan · RealPage · Apartments com Marketing & Social Platforms: Meta (FB/IG) · Google Suite · Twitter/X · TikTok · Yelp · UberEats Past Enterprise Client Impact: STIHL · Liebherr · Cushman & Wakefield ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🤝 HOW WE WORK TOGETHER ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ → Discovery call → System architecture proposal → Milestone-based delivery → MVP in 6–12 weeks, production-ready scaling in 18–32 weeks → Available 30+ hrs/week, response time within 4–8 hours → Backed by a distributed team across 🇺🇦 🇵🇱 🇮🇹 🇷🇴 🇨🇦 if you need to scale resources rapidly I prioritize long-term, high-trust partnerships over quick fixes — 29 completed projects with an average engagement of 12-24 months. Let's jump on a call and map out what you're building.

Top Rated Plus | Top 10 Machine Learning Agency on Upwork | $500K+ Earned | 8+ Years in AI & Software Engineering I'm an AI Engineer building production-grade AI agent and RAG systems, not simple prompt wrappers. With 8+ years in software engineering and AI development and $500K+ earned on Upwork, I hold Top Rated Plus status and our team is ranked among the Top 10 Machine Learning agencies on the platform. I work with companies that need an AI system to actually run in production, handle real user traffic, and stay accurate, not a demo that breaks on the first edge case. As an AI Engineer, my core work covers retrieval-augmented generation pipelines, agentic workflows with tool calling and structured outputs, prompt engineering, and LLM integration with OpenAI, Anthropic, and Gemini APIs. This is the kind of system clients need an AI Engineer for: not a chatbot that answers FAQs, but an agent that retrieves the right information, calls the right tools, and follows through on what the user actually needs, with hallucination reduction and evaluation built in from the start. As a Machine Learning Engineer and Data Scientist, I build systems for structured and time-series data: demand forecasting, anomaly detection, biomedical signal analysis, and structural health monitoring. My data scientist workflow covers Python, scikit-learn, pandas, NumPy, and SciPy alongside deep learning frameworks including TensorFlow, PyTorch, and Keras, with experiment tracking and evaluation metrics to ensure models perform consistently in production. When a project needs predictive or classification models alongside the AI agent itself, I own that layer too as a Machine Learning Engineer. As a Computer Vision Engineer and software engineer, I build object detection, multi-object tracking, pose estimation, and image segmentation systems using OpenCV, YOLO, and deep learning architectures. Where this intersects with the AI Engineer work is in multimodal systems and computer vision agents: I work with Vision Language Models (VLMs) to build AI pipelines that understand images and video, not just text. Most AI Engineers only work with text. I bring production computer vision and deep learning experience on top of the LLM layer, which matters for any product where the AI needs to see, not just read. On the engineering side, I work as a Python developer and software engineer building backend services with FastAPI, vector databases including Pinecone and pgvector, and LangChain or custom orchestration for multi-step agent logic. When a project needs full ownership of both the AI layer and the surrounding application, I work as a Full Stack AI Developer, handling backend APIs, database design, and frontend integration so the AI system ships as a complete product, not just a backend script. I work with a specialized team that includes a computer vision PhD, deep learning researchers, and mathematical optimization specialists. This lets me scope larger systems, split orchestration, retrieval, and evaluation work across the team, and deliver a full AI Engineer and Machine Learning Engineer engagement faster than a solo contributor could, with the software engineering discipline of clean APIs, logging, and testing baked in from day one. Clients typically work with me when they need: - an AI Engineer to build a RAG pipeline, AI agent, or chatbot that actually works in production - a Machine Learning Engineer or Data Scientist to build predictive models or structured data pipelines - a Computer Vision Engineer to add visual understanding or VLM-based reasoning to an AI product - a Software Engineer who understands LLM orchestration, tool calling, vector search, and backend architecture - a Python developer who can own the full stack from model training to deployed API If you need an AI Engineer and a software engineer with the full stack from prompt design to production deployment, let's talk. Main stack: Python, OpenAI API, Anthropic Claude, Gemini, LangChain, LangGraph, LlamaIndex, FastAPI, Pinecone, pgvector, TensorFlow, PyTorch, Keras, OpenCV, YOLO, Docker, PostgreSQL, JavaScript, Git.

🤖 I design AI systems · Workflow Automation · cloud data platforms & reporting architecture for companies that need reliable operations instead of disconnected tools As an AI Solutions Architect I connect n8n · Make · Zapier · Microsoft Power Automate · OpenAI API · Claude API · Azure OpenAI · Python · REST API · Webhooks · databases · Snowflake · Azure Data Warehousing & Power BI My work combines three layers AI Automation Data Integration Cloud Architecture I help when a team has manual processes between CRM platforms · ClickUp · Airtable · HubSpot · Notion · Google Sheets · internal tools · databases · APIs & reporting systems I design the full path from business process to Workflow Automation to API Integration to Database Integration to AI Agent Development to Error Handling and Monitoring to Dashboard Automation Typical projects include ▪ AI Agent Development for internal operations · support · analytics · document processing · knowledge search & repetitive tasks ▪ n8n AI Agents connected to OpenAI API · Claude API · Azure OpenAI · LangChain · Supabase · REST API · Webhooks & company data ▪ RAG Systems · AI Chatbots · Conversational AI & company knowledge workflows ▪ AI Workflow Automation for approvals · notifications · enrichment · validation · routing · synchronization · reporting & human review ▪ Business Process Automation with n8n · Make · Zapier · Microsoft Power Automate · Google Apps Script & Python ▪ CRM Automation · ClickUp Automation · lead routing · task creation · pipeline updates · reminders & reporting ▪ API Integration · Third-Party Integration · Data Integration · Database Integration · REST API workflows & custom Webhooks ▪ ETL Automation · Data Extraction · Data Transformation · Scheduled Workflows · monitoring · recovery logic & documentation ▪ Power BI Automation · Dashboard Automation · Automated Reporting · KPI logic · Data Modeling & reporting-ready databases I do not choose a tool before understanding the process n8n supports flexible Workflow Automation · self-hosted systems · custom logic · AI Agent Development & complex API Integration Make supports fast business integrations · visual workflows · SaaS connections · CRM Automation & operational prototypes Python supports Workflow Automation that requires custom algorithms · advanced Data Transformation · scale · testing or deployment control Before development I define business rules · workflow states · data ownership · access permissions · human approvals · exception handling · monitoring · retries · audit logic · deployment structure & reporting requirements A strong AI solution needs trusted data · secure Database access · clear Solution Architecture · reliable API Integration · controlled Workflow Automation · monitoring · documentation & measurable business output My architecture background includes Azure Data Warehousing · Snowflake Database · Azure Data Factory · Azure DevOps · Databricks Platform · Microsoft Azure SQL Database · Microsoft SQL Server · ETL · Data Modeling · Business Intelligence & Microsoft Power BI For Azure projects I design Azure Cloud Architecture · Azure Data Warehousing · Azure Data Factory pipelines · Azure DevOps delivery · CI/CD · Terraform · Git · deployment control & Azure OpenAI integration For Snowflake projects I work with Snowflake Database architecture · Snowflake Data Warehousing · Data Integration · ETL Automation · dbt workflows · reporting layers · access logic · performance review · cost control & Power BI connectivity One enterprise Azure engagement included scalable multi-tenant architecture · strict data isolation · Azure Databricks processing · infrastructure cost reduction & multilingual Power BI reporting Another long-term engagement expanded from Power BI reporting into automated data validation · incremental refresh · data reliability improvements & third-party service integration I connect AI Automation to the data foundation I connect reporting to reliable ETL and Database Architecture I work across Automation Architecture · Solution Architecture · Cloud Architecture · Cloud Data Architecture · Database Architecture · AI Solution Architecture & AI Data Architecture If your AI Agent cannot access trusted company data I can design the Data Integration and Database Integration layer If your Workflow Automation fails without visibility I can add Error Handling and Monitoring · retries · alerts · audit logic & recovery workflows If your reporting depends on manual exports I can connect ETL Automation · Azure Data Warehousing · Snowflake Database · Power BI Automation & Automated Reporting If your prototype needs to become a production system I can define the Solution Architecture · security model · data flow · deployment process · monitoring structure & technical roadmap Send me your process · tools · data sources & expected outcome 🏆 Wait, you’ve actually made it to the very end! Mention "1 in 100" in your first DM and I will provide a discount😉