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AI Engineer with less than a year in Machine Learning and Generative AI
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Assessing your cultural and operational fit
Highly motivated Electrical Engineering student with 11 months of experience in AI and Machine Learning. Proficient in developing and deploying ML solutions, contributing to AI application architectures, and working with deep learning frameworks. Skilled in Python, FastAPI, TensorFlow, Keras, and cloud platforms like AWS and Google Cloud. Eager to apply technical skills to solve real-world problems and optimize AI model performance.
National University of Sciences and Technology (CEME)
Bachelors of Science · Electrical Engineering
August 1, 2021 – June 30, 2025
StamoAI
AI Engineering Intern
June 1, 2026 – Present
Tbilisi, Tbilisi, Georgia
AxcelerateAI
Junior Machine Learning Engineer
July 1, 2025 – March 1, 2026
Lahore, Punjab, Pakistan
SYSVIT
Machine Learning Intern
July 1, 2024 – August 1, 2024
London, England, United Kingdom
Ha App
June 1, 2026 – Present
Contributed in FastAPI backend with health-tracking and plan-generation endpoints. Designed MongoDB schemas for logs, and AI-generated meals and exercise plans. Integrated LLMs to generate personalized meal plans and standard exercise plans.
AI-BOT
June 1, 2026 – Present
Developed an AI chatbot backend using FastAPI with REST APIs for messaging, sessions, and lead capture. Integrated Whisper-based speech-to-text to process voice input and convert audio queries into chatbot messages. Built an intelligent response pipeline using LLMs with controlled content sources for company queries and support using RAG.
Mozaico AI Spatial Visualizer
June 1, 2026 – Present
Developed an AI pipeline to analyze room images and recommend mosaic designs using Gemini prompts and catalog filters. Used Nano banana through Runware inference for realistic mosaic staging with proper lighting and perspective. Built an async FastAPI backend endpoints preprocessing, room type and size validation, and AWS S3 storage.
Virtual Staging Automation
June 1, 2026 – Present
Built an automated virtual staging agent using gemini for prompt generation and nano banana or image generation. Implemented frame extraction and multi-angle processing using qwen model for flexible workflows. Developed a multi-angle staging pipeline for consistent furniture placement.
Fish Pallet Detection
June 1, 2026 – Present
Trained a custom YOLO pallet model and deployed it on NVIDIA Jetson SoC, interfacing camera hardware input through FastAPI. Implemented Python preprocessing and post-processing routines optimized for the memory and compute limit of hardware. Integrated Prometheus + Grafana dashboards to monitor AI inference latency, GPU utilization, and detection accuracy.
AI-driven Leukemia Detection using FPGA
May 1, 2025 – May 1, 2025
Trained a MobileNetV2 CNN on blood smear images, achieving 93% accuracy. Deployed the model on a Spartan-6 FPGA with hybrid CPU/FPGA execution. Generated RTL and block diagrams using Vivado and ISE to verify performance and resource use.
CS231n: Convolutional Neural Networks for Visual Recognition
Stanford University
June 1, 2026 – Present
CS224n: NLP with Deep Learning
Stanford University
June 1, 2026 – Present
Machine Learning, AI, and Data Science
eHunar
June 1, 2026 – Present
Data Science and Analytics
HP Life, HP Foundation
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project diversity, ranging from medical image analysis (Leukemia Detection) to conversational AI (AI-BOT) and spatial visualization (Mozaico AI), indicates a broad interest and adaptability, which can contribute positively to cultural fit. The involvement in both academic and internship roles, including remote work, suggests flexibility. However, the experience level is entry-to-junior, and most projects are academic, which might require more mentorship and integration into a fast-paced industry environment. The target role of 'AI Engineer' aligns well with the candidate's demonstrated technical interests and project work.
Soft Skills & Operational Fit
The candidate's resume highlights soft skills such as Project Management, Time Management, Leadership Qualities, Teamwork Abilities, Problem Solving, Communication, Work under Pressure, and Attention to Details. While these are stated, there is no direct evidence from completed tests or detailed project descriptions to objectively assess their operational fit or the application of these soft skills in a professional setting. The academic nature of most projects limits the insight into real-world operational fit.