Help & Supportcareers@gravityer.com
AI Engineer with 1+ years in NLP, Full Stack Development & Distributed Systems
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Currently pursuing a Bachelor's in Computer Science and Engineering, I possess 1.2 years of experience in AI/ML and full-stack development. My expertise spans building sophisticated AI systems like multilingual RAG chatbots and digital image forensic classifiers, alongside developing robust web applications with FastAPI and modern frontend technologies. I am proficient in Python, PyTorch, and various data engineering and backend tools, consistently delivering high-performance, scalable solutions.
National Institute of Technology, Sikkim
Computer Science and Engineering · Computer Science and Engineering
August 1, 2022 – June 30, 2026
Premade Innovations Pvt Ltd
AI & ML Intern
June 1, 2025 – August 1, 2025
India
FOXAISR
Full Stack Web Development Intern
January 1, 2025 – March 1, 2025
India
Controller Load Balancing & Failure Recovery in SDN
January 1, 2025 – June 1, 2026
Designed and implemented the LB-Flex (Load-Balanced Flexible Programmability) framework to optimize controller failure recovery in Software-Defined Networks. Engineered a score-based selection algorithm incorporating a quadratic congestion penalty to prevent controller hotspots and ensure stable load distribution. Evaluated the framework on ATT and BELNET topologies, achieving a 28% reduction in average control-plane latency and a 45% improvement in load distribution fairness compared to baseline greedy algorithms.
View ProjectDigital Image Forensic Vision System
January 1, 2024 – June 1, 2026
Engineered a multi-modal forensic classifier integrating 2D Discrete Fourier Transform (DFT) magnitude maps with RGB data to detect high-frequency suppression artifacts unique to modern diffusion models. Fine-tuned an EfficientNet-B0 backbone via Transfer Learning on the 35,000-image GenImage dataset (Midjourney, SD, GLIDE), achieving 95.86% accuracy using AdamW optimization and BCEWithLogitsLoss. Integrated Grad-CAM heatmaps to provide forensic interpretability, visualizing "unnatural" pixel distributions, and deployed the real-time inference dashboard via Streamlit on Hugging Face Spaces.
Multi-Label Toxic Comment Classification
January 1, 2024 – June 1, 2026
Engineered a real-time Transformer-based NLP system utilizing DistilBERT to simultaneously categorize text into 6 toxicity dimensions with <100ms inference latency. Resolved severe class imbalance by implementing stratified sampling and BCEWithLogitsLoss, significantly boosting the model's sensitivity to minority threat categories, deployed via Streamlit.
NPTEL Business Intelligence & Analytics (IIT Madras) – Elite, 90% Score
NPTEL (IIT Madras)
June 1, 2026 – Present
GATE 2025 Qualified – AIR 7.9k, Score: 486
Unknown
January 1, 2025 – Present
NPTEL Computer Vision (IIT Kharagpur) – Elite + Top 1% Scorer
NPTEL (IIT Kharagpur)
January 1, 2025 – Present
Cultural Fit Analysis
The candidate's diverse academic projects (computer vision, NLP, SDN) and internships (AI/ML, full-stack web development) indicate a broad interest in technology and a willingness to explore different domains. Their involvement in a hackathon and leadership role suggests a proactive and collaborative mindset. The focus on real-world applications and measurable impact in their projects aligns well with a results-oriented culture. The candidate's continuous learning through NPTEL certifications also points to a growth mindset.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving and analytical skills through their project work, particularly in identifying and addressing challenges like class imbalance and controller hotspots. Their experience leading a team in the SIH Hackathon indicates leadership potential and ability to collaborate. The detailed descriptions of project outcomes and methodologies suggest a structured approach to problem-solving and a focus on measurable results. The candidate's ability to deploy applications (Streamlit, FastAPI) shows an understanding of operationalizing ML models.