Help & Supportcareers@gravityer.com
AI Engineer with less than a year in RAG, Computer Vision & NLP
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Highly motivated Junior AI Engineer with 8 months of hands-on experience in developing and deploying advanced AI solutions, including RAG-based legal compliance, real-time retail intelligence, and AI-powered HRMS. Proficient in Python, Machine Learning, Computer Vision, and NLP technologies, with a strong focus on building scalable and secure applications using FastAPI, Node.js, and various database systems. Adept at architectural design, model training, and performance optimization, consistently striving for impactful and innovative technical contributions.
Lovely Professional University
B.Tech · Computer Science and Engineering
August 1, 2022 – June 30, 2026
AI-Powered HRMS - Groq API
May 1, 2026 – June 1, 2026
Engineered a full-stack Human Resource Management System (HRMS) in Node.js and Vanilla JavaScript, integrating the Anthropic Claude API to automate 5 core HR workflows – resume screening, interview generation, and attrition risk prediction. Configured a resilient LLM integration layer with automatic graceful degradation, switching from Claude API inference to rule-based fallback logic during API unavailability and maintaining continuous platform availability across 50+ simulated API outage tests. Secured the platform with a hand-coded HMAC-SHA256 JSON Web Token (JWT) authentication layer and a lightweight flat-file JSON database, enforcing access controls across 4 distinct user role dashboards without external library dependencies.
Real-Time Retail Intelligence Pipeline - Computer Vision & FastAPI
April 1, 2026 – May 1, 2026
Constructed a GPU-accelerated Computer Vision pipeline using YOLOv8 object detection and ByteTrack multi-object tracking to process 5 concurrent CCTV feeds, enabling spatial occupancy analysis and brand engagement analytics across retail zones. Developed a cross-camera visual Re-Identification (Re-ID) module with HSV color-space staff filtering, eliminating tracking ID fragmentation and achieving >90% employee exclusion accuracy without custom model retraining. Deployed a FastAPI and SQLite analytics backend with a real-time spatial heatmap dashboard, ingesting high-frequency telemetry events to deliver visitor dwell-time metrics and zone-level occupancy reports.
LegalEdge AI RAG-Based Legal Compliance Assistant
January 1, 2026 – April 1, 2026
Architected a 3-stage Retrieval-Augmented Generation (RAG) pipeline – PDF ingestion, FAISS IndexFlatL2 indexing, Groq LLAMA Large Language Model (LLM) generation – covering 12,000+ segments across Indian IP, Business Law, and GST domains. Extracted and preprocessed 30+ legal PDFs via a custom Natural Language Processing (NLP) parser with token normalization and deduplication, cutting low-quality input segments by 40% before vector embedding. Tuned Groq LLaMA inference parameters – temperature, top-p, max tokens – exposing the trained model as a FastAPI REST API endpoint for production deployment, reducing hallucinated outputs across 30+ test queries spanning 3 legal domains.
View ProjectData Analysis and Visualization with Python
Coursera (Arizona State University)
May 1, 2025 – Present
Computer Networking
Coursera (Google)
May 1, 2024 – Present
Cultural Fit Analysis
The candidate's academic projects demonstrate a strong interest and proactive engagement in cutting-edge AI technologies. The diversity of projects (legal compliance, retail intelligence, HRMS) indicates adaptability and a broad understanding of AI applications. The publication achievement further underscores a research-oriented and collaborative spirit, aligning well with an innovative technical culture.
Soft Skills & Operational Fit
The candidate's project descriptions highlight skills in experimental design, research communication, and technical documentation, which are valuable for operational fit in an AI engineering role. The ability to architect complex systems and troubleshoot API integrations suggests a problem-solving mindset.