Working as an AI Software Engineer, building intelligent laboratory systems with Python-based AI integrations and cloud native solutions on AWS. Collaborating in Agile/Scrum teams, with a strong focus on GenAI, LLM Models, Agents, RAG integrations, and system observability to ensure highly reliable performance across mobile and web platforms.
- Implemented a feature that allows patients to upload medical prescriptions, utilizing AI-driven automation for data extraction and scheduling. This reduced manual workload while processing hundreds of files through Python AI workflows using LangChain, GenAI, OCR, and RAG built on highly available serverless architectures with RabbitMQ.
- Built a Flutter mobile app centralizing laboratory patient data into a unified platform, enabling automated scheduling and faster access to information by integrating GenAI, OCR, and RAG through scalable Python-based AI workflows and cloud native solutions on AWS using RabbitMQ and LangChain.
- Developed a serverless medical RAG pipeline to enable similarity retrieval of exams and medical documents, improving access to relevant clinical information, using Python, LangChain, PostgreSQL, pgvector embeddings, and AWS Lambda.
- Implemented API endpoints utilizing scalable API design principles to stream real-time LLM responses, significantly improving user experience in AI-driven workflows using FastAPI streaming capabilities.
- Developed a backend service to convert document and spreadsheet files into standardized formats and dynamically replace template tags to generate processed output files, using Python and AWS Services (Lambda, S3, ECR).
- Worked within Agile (Scrum) methodology, participating in sprint planning, daily stand-ups, sprints, and retrospectives to deliver features on schedule.
Tech Stack: Python, LLM, GenAI, RAG, Langchain, AWS (Lambda, S3, ECR, ECS, CodeCommit), PostgreSQL, Pgvector, MySQL, SQLite, RabbitMQ, Docker, Flutter, Laravel, PHP, Agile, Git.