Muhammad H.Private profile: Upwork shows its earnings only to signed-in members, so we check it every few weeks, not daily. MRR here is a rough estimate (Upwork's six-month earnings ÷ months active). Last read Nov 5, 2026.

I build reliable backend systems, AI powered products, and cloud infrastructure using AI where it creates real value. I help businesses build, scale, and modernize software with a focus on performance, security, reliability, and maintainability. My approach is simple: use AI for intelligence and development acceleration, and use strong software engineering to keep critical systems predictable, secure, and reliable. WHAT I CAN HELP YOU WITH • Backend and full stack application development • AI powered SaaS products and LLM integrations • RAG pipelines, knowledge systems, and AI agents • API development and third party integrations • Recommendation engines and personalization • Real time applications and distributed systems • Cloud infrastructure, DevOps, and deployment • Database design, performance optimization, and data pipelines • Existing system modernization and technical problem solving BACKEND AND FULL STACK DEVELOPMENT Python • Django, Django REST Framework, FastAPI, Flask, Celery Node.js and TypeScript • Express, NestJS, Fastify, Socket IO Go • Gin, gRPC, WebSockets, concurrent services PHP and Microsoft Technologies • Laravel, Symfony, WordPress, C#, ASP dot NET Core Frontend Development • React, Next js, Angular, Vue js Mobile Development • Flutter, BLoC API and Architecture • REST APIs, GraphQL, gRPC, WebSockets, Webhooks, SDKs, Microservices APPLIED AI AND LLM ENGINEERING AI Platforms • OpenAI, Claude, Gemini, Hugging Face LLM Applications • AI assistants, chatbots, AI agents, workflow automation Retrieval and Knowledge Systems • Retrieval Augmented Generation (RAG), semantic search, embeddings, knowledge retrieval Agent Engineering • Tool calling, function calling, structured outputs, prompt engineering, context engineering Personalization • Recommendation systems, user preferences, behavioral data, intelligent ranking Model and Vector Infrastructure • Fine tuning, pgvector, Pinecone, ChromaDB PRODUCTION READY AI Building a working AI demo is one thing. Making it reliable in production is another. I focus on making AI applications controlled, testable, secure, and maintainable. • LLM evaluation and output validation • Guardrails and constraint enforcement • Agent permissions and controlled tool execution • Error handling and model fallbacks • AI observability, logging, and tracing • Cost and latency optimization • Testing, reliability, and failure recovery I use AI where it creates meaningful value, not simply because it is available. Critical business rules, permissions, security checks, and important workflows remain controlled by deterministic software. RECOMMENDATION AND PERSONALIZATION SYSTEMS I build recommendation systems that combine user preferences, behavioral signals, retrieval, and ranking to deliver relevant results. • Recommendation algorithms and weighted scoring • Semantic retrieval and embeddings • Preference and constraint based filtering • Behavioral data and feedback loops • Personalized search and ranking • LLM powered personalization where appropriate My approach is to start simple, measure results, and add complexity only when it improves the product. CLOUD AND DEVOPS AWS • EC2, EKS, Lambda, RDS, S3, VPC Google Cloud • GKE, Cloud Run, BigQuery, Cloud SQL Microsoft Azure • AKS, IoT Hub, Stream Analytics Infrastructure and Deployment • Docker, Kubernetes, Linux, Nginx CI/CD and Automation • Jenkins, GitLab CI/CD, Bitbucket Pipelines Monitoring and Observability • Prometheus, Grafana, Loki, Sentry I focus on deployments that are maintainable, observable, secure, and appropriate for the project’s scale. DATABASES AND DATA ENGINEERING Relational Databases • PostgreSQL, MySQL, MariaDB, Microsoft SQL Server, SQLite NoSQL and Distributed Databases • MongoDB, CockroachDB, Redis Geospatial, Graph, and Vector Data • PostGIS, Apache AGE, pgvector Data Engineering • Database design and query optimization • ETL and data pipelines • Data processing and transformation • Web scraping and data collection REAL TIME AND DISTRIBUTED SYSTEMS • Apache Kafka and Redis • WebSockets and Socket IO • SignalR and gRPC • Event driven architecture • Background workers and asynchronous processing • Real time synchronization and messaging • Microservices and distributed workflows I design systems around the actual requirements, balancing performance, complexity, reliability, and long term maintenance. HOW I APPROACH SOFTWARE ENGINEERING Understand the problem. Simplify the solution. Design the architecture. Build the system. Validate the results. Deploy and improve. I use AI throughout development to accelerate: • Implementation and prototyping • Testing and debugging • Refactoring and code review • Technical research and problem solving • Documentation and automation AI generated code still requires engineering judgment, security reviews, testing, and proper architecture. I measure success by the quality of the delivered