Cristian Dragoi

Participated in advanced machine learning projects focused on Natural Language Processing (NLP) and Computer Vision (CV), including the development of multi-modal large language model (MLLM) solutions. Designed and implemented a Retrieval-Augmented Generation (RAG) architecture combining text and image data, utilizing Qdrant and Elasticsearch, which improved service accuracy from 87% to 96%. Developed and deployed scalable microservices using Flask and Azure Function Apps. Built robust, event-driven data pipelines leveraging Azure Blob Storage and Azure Databricks, supporting automated data ingestion and processing. Applied Test-Driven Development (TDD) practices with pytest and unittest, beginning with edge-case definitions to ensure reliability and code quality. Performed ETL processes using pandas for data cleaning, transformation, and integration, and optimized numerical operations and pipeline performance with NumPy.

Developed and maintained high-performance RESTful APIs using Python and FastAPI, improving system scalability and response times. Optimized database operations with SQLAlchemy ORM and implemented ETL data pipelines integrating PostgreSQL and MongoDB. Applied Redis caching to enhance performance and used AWS services (EC2, Lambda, S3) for scalable cloud deployment. Built and managed CI/CD pipelines with GitHub Actions, automating testing and deployment processes. Integrated machine learning models using TensorFlow and PyTorch to deliver AI-driven features. Designed and maintained containerized environments with Docker and orchestrated services using Kubernetes. Delivered Generative-AI solutions by tailoring and fine-tuning models to meet specific business and product requirements.

Built and maintained a Python backend based on service-oriented architecture, supporting RESTful APIs with Flask and FastAPI. Optimized database performance using SQLAlchemy ORM and improved runtime efficiency by developing Cython (C/C++) extensions, achieving up to 80% performance gains. Implemented RAG architectures with Qdrant and OpenAI embeddings. Developed responsive front-end applications with React and integrated them with backend APIs to ensure smooth data flow. Designed and implemented ETL pipelines aggregating data from PostgreSQL and MongoDB, including document extraction modules and internal SDKs. Implemented custom caching with Redis and applied event-driven architectures using AWS SAM, Lambda, and Fargate. Deployed and managed applications using AWS services (EC2, S3, RDS, SNS, SQS, EKS, ECR) and maintained Kubernetes clusters. Improved system reliability using Grafana and Prometheus, reducing error rates by 30%. Integrated machine learning models with transformers and PyTorch and delivered tailored Generative AI solutions. Established CI/CD pipelines with GitHub and Docker, including automated testing and linting.

Developed and maintained full-stack web applications using Python (Django, Django REST Framework) and JavaScript/React, contributing to both backend and frontend development. Participated in the architectural design of end-to-end systems and implemented asynchronous processing and multithreading to efficiently handle CPU- and I/O-bound tasks in distributed environments. Improved overall service performance by up to 40% through the design and application of optimized data structures and algorithms. Applied Object-Oriented Programming principles and SOLID design practices to build modular, maintainable, and scalable software. Collaborated closely with frontend teams to deliver interactive user interfaces using React and Redux, leveraging component-based architecture and state management. Generated and maintained API documentation using Swagger/OpenAPI to support seamless team collaboration. Conducted code reviews, mentored junior developers, and identified potential technical issues. Contributed to Proof of Concept (POC) initiatives to validate technical solutions. Designed real-time data pipelines for time-series data and performed data preprocessing using NumPy and Pandas.

Developed and maintained server-side applications and services using Python, focusing on modularity, scalability, and maintainability. Built robust data processing pipelines and integrated external systems using libraries such as Pandas, NumPy, and SQLAlchemy. Actively participated in the full software development lifecycle, including requirements analysis, system design, implementation, deployment, and ongoing maintenance. Worked extensively with relational databases including MySQL, PostgreSQL, and SQLite, designing efficient schemas and optimizing queries for performance. Wrote clean, efficient, and well-documented Python code in accordance with best practices and coding standards. Ensured seamless integration with external APIs and third-party systems through RESTful services and structured data formats such as JSON and XML. Collaborated closely with frontend developers to integrate backend services with user-facing applications, ensuring reliable data exchange and smooth system operation.

Developed and maintained Python-based applications with a focus on writing clean, efficient, and well-documented code. Collaborated closely with senior developers to implement new features, resolve bugs, and optimize performance within existing codebases. Contributed to the development and enhancement of web applications using frameworks such as Flask. Assisted in database management tasks, including writing queries and integrating PostgreSQL and MySQL databases. Participated in code reviews and benefited from mentorship to strengthen coding practices and adopt industry best practices. Used Git for version control, contributing to team-based development and managing code changes effectively. Ensured application reliability and quality by implementing automated tests with pytest and unittest.