AI Engineer with 2+ years in Generative AI & NLP
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Assessing your cultural and operational fit
Aspiring Generative AI Engineer and 3rd-year CSM undergraduate specializing in Natural Language Processing (NLP) and Large Language Models (LLMs). Hands-on experience architecting production-ready AI systems, including low-latency RAG pipelines, legal intelligence frameworks, and secure NLP routing engines. Proficient in Java (DSA), Python, and building scalable production backends.
Keshav Memorial Engineering College, Osmania University
B.E. · CSM (Artificial Intelligence & Machine Learning)
August 1, 2023 – June 30, 2026
Urbane Junior College
Intermediate · MPC
June 1, 2021 – May 31, 2023
Johnson Grammar School
ICSE Class 10
N/A – May 31, 2021
Intelligent Support Ticket Router
January 1, 2026 – June 1, 2026
Architected and deployed an automated routing engine using spaCy to perform intent classification and priority mapping for incoming customer support tickets. Implemented Custom Named Entity Recognition (NER) to extract critical metadata (product types, order IDs) to dynamically route tickets to specialized support queues. Integrated a PII Shield layer to automatically detect and redact sensitive customer data (credit card numbers, personal identifiers) before database storage, ensuring compliance with data privacy standards. Built a high-performance backend using FastAPI, achieving low-latency asynchronous API endpoints for seamless system integration.
Advocadabra - Legal AI Assistant
January 1, 2025 – December 1, 2025
Developed a specialized Legal AI framework for Similar Case Retrieval (SCR) and Precedent Case Recommendation (PCR). Built Legal Judgment Prediction (LJP) models using advanced text classification and NLP frameworks to forecast judicial outcomes. Authored an academic research paper detailing the system architecture, embedding strategies, and performance evaluation metrics under the guidance of an academic mentor.
Finance GPT
January 1, 2024 – December 1, 2024
Engineered a production-grade RAG pipeline to provide high-fidelity answers to complex financial queries from structured and unstructured sources. Automated insight extraction from financial statements, earnings calls, and market reports using quantized Large Language Models (LLMs). Implemented optimized vector embeddings and indexing to ensure low-latency document retrieval and highly context-aware responses. Mitigated model hallucinations by fine-tuning prompts and validation logic, significantly improving accuracy regarding domain-specific financial terminology.
Cultural Fit Analysis
The candidate's projects demonstrate a strong interest and specialization in AI/ML, particularly NLP and LLMs, which aligns well with an AI Engineer role. The diversity of academic projects (support ticket routing, legal AI, finance GPT) shows a breadth of application areas for AI. However, the lack of professional experience and team-based projects makes a comprehensive cultural fit assessment challenging.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work on complex, multi-faceted AI problems. The academic research paper suggests strong technical writing and analytical skills. However, without direct work experience or psychometric test results, it is difficult to assess operational fit, teamwork, or stress handling capabilities.