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AI Engineer with less than a year in NLP, Computer Vision & RAG
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Assessing your cultural and operational fit
AI Engineer with a Master's in Data Science, specialising in NLP and Computer Vision. Production experience building RAG pipelines, agentic workflows, and Transformer-based HTR systems. Skilled across the full ML lifecycle from fine-tuning LLMs with PEFT to deploying models via FastAPI and Docker. Proven ability to deliver results in low-resource and high-complexity language settings.
University of Kerala
MSc · Data Science
August 1, 2023 – June 30, 2025
University of Kerala
BSc · Mathematics
August 1, 2020 – June 30, 2023
ICFOSS
AI Research Intern
November 1, 2025 – Present
India
ZenturioTech, Technopark
AI Intern
April 1, 2025 – June 1, 2025
India
Hybrid Handwritten Text Recognition System
June 25, 2026 – Present
Architected a hybrid HTR pipeline combining Swin Transformer for spatial feature encoding, Conformer for sequence modelling, and a Transformer-based decoder with a Cross-Attention Fusion Module to integrate both representations. Achieved CER 5%, WER 10% on a standard handwritten text benchmark.
View ProjectMalayalam PDF Question Answering System
June 25, 2026 – Present
Built a verbatim RAG pipeline — layout-preserving OCR, paragraph-aware chunking, multilingual embeddings (paraphrase-multilingual-mpnet-base-v2), and similarity-based retrieval across scanned and native Malayalam documents. Integrated a web search fallback (DuckDuckGo/Bing) for out-of-document queries; deployed as a full-stack app with React frontend and Django backend, returning exact, non-hallucinated answers.
View ProjectResearch Paper Automation Agent
June 25, 2026 – Present
Built a LangGraph-based agentic pipeline that searches arXiv and Semantic Scholar (141 papers retrieved), auto-ranks and downloads PDFs, and runs LLaMA-3.3-70B analysis on the top 25 generating architecture-level insights and CER/WER trend reports; reduced manual paper review time by ~70%.
View ProjectMachine Learning with Python
ICFOSS
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic projects and internships demonstrate a strong interest in cutting-edge AI research and practical application, aligning well with an AI Engineer role. The diversity of projects (HTR, Q&A, agentic systems) and exposure to different tools (LangChain, Groq, Streamlit, Django) suggest adaptability and a broad technical curiosity. The focus on open-source tools and research-oriented tasks indicates a fit for an innovative and learning-driven environment.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a strong problem-solving aptitude and an ability to deliver tangible results (e.g., reduced manual review time, improved CER/WER). The academic and internship experiences suggest a collaborative approach to research and development. The detailed project descriptions reflect good communication of technical work.