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AI Engineer with less than a year in LLMS and RAG pipelines.
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CS undergraduate specialising in AI & ML (CGPA 8.4), graduating Aug 2026, based in Hyderabad. Strong Python foundation with genuine interest in LLMS, RAG pipelines, NLP, and product engineering. Built two production-grade, full-stack systems solo including an AI-driven supply chain intelligence platform featuring a probabilistic ML engine, REST APIs, and an analytics dashboard. Experienced with React, MySQL/relational databases, REST API design, Spring Boot, and Git-based collaborative workflows. Eager to contribute to Tektalis' July 2026 AI Product Engineering cohort and build real-world AI products.
Guru Nanak Institutions Technical Campus (GNITC)
B.Tech · Computer Science & Engineering (AI & ML)
August 1, 2023 – June 30, 2026
Government Polytechnic Vikarabad
Polytechnic Diploma · Electrical & Electronics Engineering
August 1, 2020 – June 30, 2023
NexusFlow Autonomous AI Supply Chain Risk Intelligence System
January 1, 2025 – December 31, 2025
Built an AI-driven risk intelligence platform end-to-end solo – shipment tracking, supplier management, ML risk scoring, delay prediction, analytics dashboard, and role-based notifications – demonstrating full product engineering ownership from backend to UI. Engineered a probabilistic delay prediction engine (base probability from historical data + live-status adjustment + confidence scoring) – applying core ML and statistical modelling principles directly relevant to LLM-powered product pipelines. Designed a weighted multi-factor scoring algorithm (6 features, tuned weights) classifying outputs in real time – mirrors RAG retrieval ranking and NLP scoring logic used in LLM products. Built a React 18 analytics dashboard consuming aggregated SQL views via REST; enforced JWT + RBAC security; documented with Swagger/OpenAPI; tested via Postman – production-grade stack aligned with modern AI product engineering.
View ProjectRetail Ordering Website - Full-Stack E-Commerce Platform
January 1, 2024 – December 31, 2025
Delivered complete full-stack platform solo – React 18 frontend, REST API backend, normalised database, RBAC access control, and Swagger-documented endpoints – demonstrating readiness for cross-layer AI product development. Applied structured Git branching, Postman testing, and layered architecture – engineering discipline directly applicable to team-based product sprints.
View ProjectCultural Fit Analysis
The candidate's projects showcase a strong interest and practical application in AI/ML, aligning well with an AI Engineer role. The solo development of complex systems like NexusFlow indicates a drive for end-to-end product ownership. The academic background in AI & ML further reinforces this fit. However, the lack of diverse team projects or professional experience limits the assessment of broader cultural fit and collaboration in a corporate setting.
Soft Skills & Operational Fit
The candidate demonstrates strong independent project ownership and full-stack development capabilities, indicating a proactive and self-sufficient work attitude. The detailed project descriptions suggest good communication of technical work. However, without specific psychometric test results, a comprehensive assessment of stress handling, logical reasoning, and team collaboration is not possible.