Internal transfer from AVASOFT to zeb, continuing technical ownership and hands-on development of the same production AI projects. • Continued leading the end-to-end technical delivery of the AI-driven healthcare/pharmacy and AI-driven invoice extraction projects following the internal organizational transfer. • Continued hands-on development while owning architecture, implementation, integration, troubleshooting, performance optimization, production support, and delivery. • Continued client-facing responsibilities, including requirements discussions, technical solution discussions, progress communication, issue resolution, and coordination throughout delivery. • Continued development and enhancement of AI-powered healthcare workflows, including LLM integration, automation, validation, and Human-in-the-Loop processes. • Continued development and support of the AI-driven invoice extraction solution using AWS Lambda and DynamoDB for AI-powered invoice processing and structured data storage. • Worked closely with internal engineering teams and stakeholders to resolve production issues, deliver enhancements, and maintain system reliability. • Continued contributing to Agentic AI, Generative AI, LLM applications, backend services, and AWS-based production architectures.

• Led and developed two production AI projects end-to-end, owning technical architecture, hands-on implementation, integration, testing, troubleshooting, client communication, delivery, and production support. • Continued hands-on development while leading the technical implementation of AI-powered healthcare/pharmacy and invoice extraction solutions. • Led the AI-driven healthcare/pharmacy platform, designing and developing AI workflows to support patients and pharmacists while reducing pharmacist review effort by 40%+. • Led the AI-driven invoice extraction solution, designing and developing the AI processing workflow using AWS Lambda and DynamoDB for invoice extraction and structured data storage. • Owned technical discussions with clients, including requirements clarification, solution discussions, progress updates, issue resolution, and communication throughout project delivery. • Translated client requirements into technical designs and guided implementation decisions across AI workflows, backend services, databases, and AWS infrastructure. • Worked hands-on with LLM integrations, Agentic AI workflows, RAG, tool/function calling, structured outputs, validation, error handling, and production reliability. • Investigated and resolved technical and production issues, performed performance tuning, and supported successful delivery of the solutions. • Coordinated with internal engineering teams and stakeholders to drive development, testing, deployment, and production support.

• Developed production-grade Generative AI and Agentic AI applications using Python, AWS, LLMs, LangChain, LangGraph, Pydantic AI, and FastMCP. • Developed a Multi-Agent Customer Data Management system using Agentic AI workflows, tool/function calling, structured outputs, and integration with enterprise systems. • Developed an enterprise RAG chatbot using retrieval, embeddings, vector search, and LLM-based response generation to provide context-aware answers from business data. • Contributed to the development of an AI-driven healthcare/pharmacy platform, implementing AI-powered workflows to support patients and pharmacists and reduce manual review effort. • Developed an AI-driven invoice extraction workflow using AWS Lambda and DynamoDB to process invoice data and store structured extraction results for downstream processing. • Developed AI-driven insurance validation workflows, applying LLM-based processing and business validation logic to automate insurance-related verification. • Built and integrated custom MCP/FastMCP tools to enable AI applications and agents to interact with internal systems and backend services. • Implemented RAG and LLM application components using embeddings, vector databases, structured outputs, prompt engineering, and retrieval workflows. • Worked on production backend services and cloud infrastructure using Python, FastAPI, AWS services, databases, and event-driven architectures.

• Built hands-on experience in Python, SQL, Generative AI, prompt engineering, and backend application development. • Worked on AI/ML application development and RAG-based workflows, connecting business data with LLM-powered applications. • Developed backend functionality using Python and contributed to frontend development using React. • Worked with structured data processing, SQL, and AI application workflows to build a foundation in end-to-end software and AI engineering.

• Worked on a machine learning project for calories-burned prediction, gaining hands-on experience with ML workflows and model development. • Applied data preprocessing and machine learning techniques to build and evaluate predictive solutions.