We can organize an interview with Aldin or any of our 25,000 available candidates within 48 hours. How would you like to proceed?
Schedule Interview NowAt Softaims, I have been fortunate to work in an environment that values creativity, precision, and long-term thinking. Each project presents a unique opportunity to transform abstract ideas into meaningful digital experiences that create real impact. I approach every challenge with curiosity and commitment, ensuring that every solution I design aligns not just with technical requirements, but also with human needs and business objectives. One of the most rewarding aspects of my journey here has been learning how to bridge the gap between innovation and practicality. I believe technology should simplify complexity, enhance efficiency, and empower people to do more with less friction. Whether building internal systems, optimizing workflows, or helping bring client visions to life, my focus remains on developing solutions that stand the test of time. Softaims has encouraged me to grow beyond coding—to think about design, communication, and sustainability in technology. I see every project as part of a larger ecosystem, where small details contribute to long-lasting results. My daily motivation comes from collaborating with people who share the same passion for doing meaningful work, and from seeing the tangible difference our efforts make for clients around the world. More than anything, I value the culture of learning and improvement that defines Softaims. It’s a place where ideas evolve through teamwork and constructive feedback. My goal is to continue refining my craft, exploring new approaches, and contributing to solutions that are not only efficient but also elegant in their simplicity.
Main technologies
14 years
3 Years
11 Years
2 Years
Potentially possible
I built RescueVision, an end-to-end, multi-modal AI system to accelerate search and rescue (SAR) operations. I trained a YOLOv8n object detection model on over 2,200 aerial images, achieving a Precision of 85.3%, Recall of 76.2%, and mAP50 of 0.833. The system also includes a private RAG pipeline built with LangChain and ChromaDB. The entire project was developed with a robust microservices architecture, integrating Flask-based AI services with a React/TypeScript frontend.
Built an end-to-end, full-stack platform to transform unstructured customer feedback into actionable business intelligence. I developed an advanced Retrieval-Augmented Generation (RAG) pipeline using Sentence-Transformers for embeddings and a persistent ChromaDB knowledge base (10,000+ items). The platform features Intelligent Document Processing with LLM-based chunking and Tesseract OCR for comprehensive data ingestion (.pdf, .docx). The Groq LPU-powered LLaMA 3 model provides high-speed conversational analysis (average time-to-first-token <150ms).
I built a Prescriptive Maintenance system that goes beyond prediction by using a Retrieval-Augmented Generation (RAG) pipeline to prescribe solutions via a conversational AI assistant. It includes an end-to-end MLOps lifecycle, a Human-in-the-Loop (HITL) framework, and Docker containerization. I achieved an RMSE of 15.82 and 95% Recall. The system's private RAG pipeline uses Ollama, ChromaDB, and LangChain.
I built AuraScanAI, an end-to-end computer vision system demonstrating a full MLOps lifecycle for vehicle damage assessment. I custom-trained a Vision Transformer (ViT) on over 15,500 images, fine-tuning a vit_base_patch16_224 model to achieve a best validation loss of 248.27. The system features a professional MLOps workflow using Docker and Git LFS for model management, and a full-stack architecture with a Flask/PyTorch API on Hugging Face Spaces and a React/TypeScript frontend on Vercel. The backend also includes a Business Rule Engine for severity classification and repair costs.
Developed a full-stack, end-to-end AI agent capable of answering complex questions with up-to-date, sourced information. This project utilizes modern AI agent architecture, achieving a 100% task success rate and 67% faster responses by leveraging a robust, orchestrated Retrieval-Augmented Generation (RAG) workflow with a local LLM (Phi-3).
Bachelor of Science (BS) in Electrical and Electronics Engineering
2010-01-01-2014-01-01