Piotr Dziurewicz

Designed and delivered CRM and ERP-style applications using Node.js (Nest.js/Express), React with TypeScript, and AWS CDK. Automated business processes with serverless workflows including AWS Lambda, Step Functions, and SQS, integrated AI-driven solutions using OpenAI and LangChain. Developed REST and GraphQL APIs with third-party service integrations like Stripe, Firebase, Twilio, and HubSpot. Led microservices deployments on AWS ECS and Kubernetes with Terraform and AWS CDK automation. Optimized databases including PostgreSQL, MySQL, MongoDB, and DynamoDB for scale and performance. Implemented enterprise authentication and authorization protocols (OAuth2, JWT, RBAC, SSO). Collaborated closely with business teams to design tailored technical solutions. Mentored engineers and enforced TDD/BDD practices using Jest, Cypress, and PyTest achieving over 85% test coverage. Enhanced CI/CD pipelines cutting deployment times by 40%, and supported multi-cloud strategies across AWS and Azure ensu

At IBM, I designed and built production Spark and PySpark ETL pipelines managing terabyte-scale structured and unstructured data with optimizations improving job times by over 35%. I developed an LLM-powered support suite integrating various AI providers, reducing handling time by 55% and enhancing customer satisfaction. I delivered a RAG chatbot serving over 2 million users with multilingual query capabilities and secured internal knowledge base retrieval. I architected modular FastAPI microservices using gRPC, containerized with Docker and orchestrated on AWS EKS for scalable workloads. PostgreSQL, MongoDB, and Redis optimizations cut latency by 70% for high throughput loads. I led automation of ML workflows using Airflow and MLflow for tracking and rollout governance. Developed AI recommenders boosting user engagement significantly through A/B tests and implemented GraphQL and REST APIs powering investor-style analytics dashboards. I also introduced real-time monitoring dashboards,

Led the design and implementation of Spark and PySpark ETL pipelines for terabytes of structured and unstructured data, optimizing for real-time analytics. Developed LLM-powered support suites integrating OpenAI, Anthropic, and Hugging Face, which reduced average handling time by 55% and improved customer satisfaction. Delivered RAG chatbots serving over 2 million users with secure multilingual Q&A. Architected modular microservices using FastAPI and gRPC, containerized and orchestrated on AWS EKS. Tuned databases and caching layers reducing latency by 70%. Automated ML workflows with Airflow and MLflow for tracking and governance. Built AI-driven recommendation systems lifted engagement significantly. Developed GraphQL and REST APIs driving analytics dashboards and investor-style deal matching using vector search and classical ranking techniques. Led CI/CD and infrastructure as code initiatives, improving deployment efficiency and incident response. Ensured GDPR compliance for data lo