AI Engineer with less than a year in Deep Learning & NLP
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Assessing your cultural and operational fit
Aspiring AI Engineer with a BE in Artificial Intelligence & Data Science (CGPA: 7.72/10) and hands-on internship experience in computer vision and NLP. Skilled in building RAG systems, multi-agent architectures, and NL2SQL chatbots using LLMs, LangChain, and FastAPI. Proficient in PyTorch, TensorFlow, Hugging Face, ONNX, Streamlit, and vector databases.
Zeal College of Engineering, Pune SPPU University
BE · Artificial Intelligence and Data Science
January 1, 2022 – January 1, 2025
AI Unika Technologies
AI Intern
September 1, 2025 – March 1, 2026
India
Multi-Agent Research Agentic System
September 1, 2025 – June 1, 2026
Architected multi-agent RAG pipeline (planner/searcher/writer) using FAISS + vector DB for semantic retrieval. Deployed all-MiniLM-L6-v2 transformer for dense embeddings and similarity search. Integrated TAVILY Search API for live content ingestion into vector database. Integrated Groq API + Llama 3 (local/GROQ) for retrieval-augmented generation (RAG) and LLM-based reasoning. Developed an interactive Streamlit dashboard enabling natural language query input, real-time agent trace visualization, and automated report export. Optimized FAISS with IVF-PQ indexing, cutting retrieval latency by 35% and API costs by 25%.
NL2SQL Chatbot Project (Vanna 2.0 agent, Gemini-API)
September 1, 2025 – June 1, 2026
Developed a secure FastAPI chatbot converting natural language to SQL queries, processing over 100 requests daily with 95% SELECT-only compliance. Designed SQLite clinic DB (200+ patients, 500+ appointments, 5 tables) with weighted dummy data. Optimized FAISS performance using IVF-PQ indexing, achieving a 35% reduction in retrieval latency and a 25% decrease in API expenditures. Automated Plotly charting (bar/scatter/line) with heuristic selection, eliminating manual viz effort for 5 report types. Pre-seeded 15 Q&A pairs using Vanna 2.0, cutting LLM inference time by 50% for 80% of common queries.
Cultural Fit Analysis
The candidate's projects are highly relevant to an AI Engineer role, focusing on cutting-edge areas like RAG, multi-agent systems, and NL2SQL. The diversity of projects (computer vision, NLP, RAG, chatbots) indicates a broad interest and adaptability within the AI domain. The internship experience, though future-dated, aligns well with practical application of AI skills. The candidate's academic background in AI and Data Science further strengthens the cultural fit for an AI-centric organization. However, the lack of team-based project descriptions or collaborative experiences makes it difficult to fully assess cultural fit beyond technical alignment.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through project optimizations (e.g., FAISS latency reduction, inference time reduction). The project descriptions suggest an ability to work independently on complex technical challenges. However, without specific psychometric or English test scores, it's difficult to assess communication clarity, teamwork, or stress handling capabilities directly. The detailed project descriptions indicate a structured approach to development and a focus on performance metrics.