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AI Engineer with less than a year in LLM integration and software development.
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Aspiring AI Engineer focused on the intersection of software development and machine learning. My interest in designing, building, and deploying intelligent systems, with hands-on experience in LLM integration via LangChain. Eager to contribute to R&D initiatives that leverage emerging AI technologies to solve complex business challenges.
Graphic Era Hill University,Dehradun
Master of Computer Applications
August 1, 2025 – June 30, 2027
Dav College, Abohar
Bachelor of Science
N/A – June 30, 2025
Disaster-Evacuation system
June 1, 2026 – June 30, 2026
Developed a dynamic frontend interface using React to visualize evacuation routes. Engineered a CSV-based knowledge database, ensuring clean and structured input for the system's backend processing. Assisted in backend, Testing and workflow documentation.
BizBot - Local RAG Chatbot
June 1, 2026 – June 30, 2026
Developed a business-focused RAG chatbot capable of answering domain-specific queries such as order tracking and product availability, using a local Qwen3 LLM without reliance on third-party cloud APIs. Engineered a retrieval pipeline with LangChain, combining a custom system prompt and few-shot examples to guide model tone and accuracy across diverse business queries. Implemented conversation history memory to maintain context across multi-turn interactions, enabling natural follow-up questions within a session. Built an interactive Streamlit interface accessible to non-technical users, with a clean chat UI backed by a structured business knowledge base.
Cultural Fit Analysis
The candidate's academic projects demonstrate an interest in both practical application (BizBot) and societal impact (Disaster-Evacuation system), suggesting a diverse problem-solving approach. The pursuit of a Master's degree indicates a commitment to continuous learning. The focus on LLM integration and RAG aligns well with current industry trends in AI. However, the lack of professional experience and limited project diversity beyond academic settings might require additional mentorship for integration into a fast-paced industry environment.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work on different aspects of a system (frontend, backend assistance, documentation) and an interest in solving business challenges with AI. However, without specific psychometric or English test scores, it's difficult to assess communication clarity, logical reasoning, work attitude, stress handling, or team collaboration beyond what's inferred from project descriptions.