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Generative AI Engineer with 1+ years in LLM Applications & AI Agent Development
Electrical Engineering student and AI/ML engineer focused on building and deploying generative AI applications. Experienced in developing LLM-based conversational agents with LangChain and the OpenAI SDK, self-hosting and serving open-source LLMs and vision-language models (VLMs) for real research use cases, and integrating AI models into FastAPI and Streamlit based applications. Background in deep learning research (PyTorch, computer vision) provides a strong foundation in AI/ML fundamentals, data pipelines, and model evaluation. Comfortable working across the AI stack, from prompt engineering and API orchestration to deployment on local GPU servers and edge devices.
SYIAH KUALA UNIVERSITY
Bachelor · Electrical Engineering
August 1, 2022 – Present
Syiah Kuala University
AI Researcher of Coffee Bean Grading System
August 1, 2025 – Present
Banda Aceh, Aceh, Indonesia
KROENG (Community of Robotics & Electrical Engineering)
Vice Leader
February 1, 2024 – February 1, 2025
Banda Aceh, Aceh, Indonesia
Syiah Kuala University
Electronic Laboratory Assistant
September 1, 2023 – Present
Banda Aceh, Aceh, Indonesia
Jeski, Conversational AI Assistant
January 1, 2026 – Present
Built a context-aware chatbot using LangChain and the OpenAI SDK, with a custom memory system supporting multi-turn conversations. Designed and iterated on prompts to shape assistant behavior, tone, and response quality. Deployed the assistant through a self-hosted, publicly accessible web interface.
Self-Hosted LocateAnything (Vision-Language Model)
January 1, 2026 – Present
Deployed NVIDIA's LocateAnything VLM on a local RTX 6000 Ada GPU server. Built a Streamlit interface for remote, cross-device access over a public network. Gained hands-on experience with GPU server infrastructure and API/package routing for serving large AI models without cloud dependency.
Self-Hosted MedGemma (Medical LLM)
January 1, 2026 – Present
Deployed Google's MedGemma 1.5 locally using Ollama and OpenWebUI to analyze medical images such as skin lesions, MRI/CT inputs. Served a research team's skin-analysis workflow without relying on cloud infrastructure, demonstrating practical, cost-efficient deployment of open-source medical LLMs.
Face Recognition Attendance System
January 1, 2026 – Present
Led AI integration: deployed and connected a face-recognition model to cloud infrastructure and microcontroller hardware. Achieved 98% recognition accuracy under proper lighting conditions, with an average processing time of 3 seconds.
Two additional papers on Mosaic Packed Augmentation and the Coffee Grading System
Unknown
June 1, 2026 – Present
Comparative Study of Object Detection Models for Dense Coffee Bean Defect Detection Using YOLOv11, Faster R-CNN, and RetinaNet. Accepted - AIMS 2025, AJT Press.
AIMS (AJT Press)
January 1, 2025 – Present
Achieved 76% on the Data Scientist — Artificial Intelligence test, demonstrating a solid grasp of core AI/ML principles, though with room for improvement in specific areas.
Strengths
Limitations
Cultural Fit Analysis
The candidate's profile shows a strong inclination towards self-initiated projects and research, indicating a proactive and curious mindset. Their involvement in a robotics and engineering community and as a lab assistant suggests a collaborative spirit and willingness to contribute to a team environment. The diversity of projects, from conversational AI to medical LLMs and object detection, demonstrates a broad interest in AI applications. The target role of Generative AI Engineer aligns well with their project experience in LLM applications and VLM deployment.
Soft Skills & Operational Fit
The candidate's experience as a Vice Leader and Electronic Laboratory Assistant suggests leadership potential, organizational skills, and the ability to guide and mentor others. Their self-initiated projects demonstrate strong initiative and problem-solving capabilities. The psychometric test score of 294/500 (58.8%) suggests average logical reasoning, work attitude, stress handling, and team collaboration, which could be an area for development.
Scored 88% on the Python Internship Test, indicating a very strong command of Python programming, including algorithms, data analysis, and testing.
Strengths
Limitations