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Assessing your cultural and operational fit
I build AI systems that actually work in the real world — not just models that score well on benchmarks, but end-to-end pipelines that ingest messy data, learn from it, and deliver predictions that drive real decisions. Currently an AI/ML Engineer Intern at Rubixe, building and deploying ML and deep learning models for real client problems using Python, TensorFlow, and PyTorch. My technical stack: Python · TensorFlow · PyTorch · Scikit-learn · LangChain · RAG Pipelines · LLMs · Computer Vision (CNNs) · AWS · SQL · Power BI I'm especially excited about Generative AI — specifically how LLMs and RAG architectures solve real business problems: smarter search, automated analysis, and AI assistants that actually understand context. Open to full-time roles as an AI/ML Engineer, AI Engineer and data Scientist — let's connect.
University of Calicut
BSc · Computer science and electronics
May 31, 2021 – April 11, 2024
Cultural Fit Analysis
The candidate's projects demonstrate a focus on practical applications of AI/ML, which aligns with an 'AI Automation Engineer' role. The diversity of projects (chatbot, churn prediction, computer vision) shows a breadth of interest within AI. However, the experience is limited to an internship, and there's no information on team dynamics or collaborative environments to fully assess cultural fit.
Soft Skills & Operational Fit
The candidate lists problem-solving, team collaboration, analytical thinking, and quick learning as soft skills. The project descriptions indicate some level of independent work and problem-solving in building AI/ML solutions. However, without interview data or specific examples, it's difficult to fully assess the depth of these soft skills and their operational fit.