Help & Supportcareers@gravityer.com
AI Engineer with less than a year in RAG Systems & FastAPI Backend
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI/ML fresher with hands-on experience in building production-grade RAG systems, LLM-powered applications, and scalable FastAPI backends. Skilled in vector search, semantic retrieval, and async API development with strong foundations in machine learning and data engineering. Proven ability to optimize system performance, reduce memory footprint, and deploy efficient AI pipelines.
KCC Institute of Technology & Management
Bachelor of Technology (BTech) · Computer Science & Engineering (AI/ML)
October 1, 2021 – June 1, 2025
Omnie Solutions
AI/ML Intern
September 1, 2025 – December 1, 2025
Noida, Uttar Pradesh, India
Edunet Foundation
AI & Data Analytics Intern
December 1, 2024 – January 1, 2025
India
Edunet Foundation
Emerging Technologies Intern
July 1, 2024 – August 1, 2024
India
DocuSenseAI v3
June 1, 2026 – Present
Architected a privacy-first Corrective RAG (CRAG) system enabling natural language querying over local files without mandatory pre-indexing. Reduced LLM memory usage by 60% by optimizing inference pipeline (Llama-3 → Phi-3), enabling deployment on low-resource systems (<4GB RAM). Enforced a structured data ingestion pipeline for multi-sheet files (PDF, Excel, CSV), improving retrieval accuracy with source-level citations.
FastAPI E-Commerce Backend
June 1, 2026 – Present
Engineered a production-ready REST API for product catalog, user management, and order processing workflows. Implemented async database operations and transaction-safe checkout, preventing race conditions under concurrent usage. Created modular backend architecture to support scalability and future microservices expansion.
View ProjectVoxGuard AI
June 1, 2026 – Present
Developed an audio intelligence pipeline to transcribe and summarize long-form media using map-reduce chunking. Built a semantic search dashboard using vector embeddings for querying historical video/audio content with natural language queries. Applied an acoustic Trust Score algorithm using Librosa and Pyannote to detect and flag low-quality audio segments, reducing LLM hallucination risk in downstream outputs.
Getting Started with Enterprise-Grade AI
IBM SkillsBuild
June 12, 2026 – Present
Getting Started with Enterprise Data Science
IBM SkillsBuild
June 12, 2026 – Present
Cultural Fit Analysis
The candidate's projects and internships demonstrate a strong interest and hands-on experience in AI/ML, RAG systems, and backend development, which aligns well with an AI Engineer role. The diversity of projects (privacy-first RAG, e-commerce backend, audio intelligence) indicates a broad technical curiosity and adaptability. The focus on optimizing resource usage and improving accuracy suggests a results-oriented mindset. However, all experience is at an intern level, and the candidate is still pursuing a bachelor's degree, which might indicate a need for mentorship and structured guidance in a senior role.
Soft Skills & Operational Fit
The candidate's project descriptions highlight problem-solving (e.g., reducing LLM memory, preventing race conditions) and a focus on practical, deployable solutions. The internship experiences suggest an ability to work within structured environments and contribute to real-world projects. However, without specific behavioral assessment data, a deeper analysis of soft skills like teamwork, leadership, or conflict resolution is not possible.