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Aman K.

Aman K.

Ml (Machine Learning) · Mid-level

Available profile

Professional Overview

I am an AI Engineer currently advancing his expertise through a Master of Science in Computer Science at the University of Eastern Finland. My professional journey is characterized by a deep focus on the practical application of artificial intelligence, from research to production.I possesses extensive hands-on experience in designing and implementing intelligent systems across various domains. His core competencies lie in Natural Language Processing—where I had built advanced systems using Transformers, LangChain, and RAG—and Computer Vision, with significant projects in OCR, image segmentation, and object detection using models like YOLO and UNet. A key aspect of my skill set is MLOps; I am proficient in containerizing applications with Docker, orchestrating with Kubernetes, and building robust CI/CD pipelines to ensure scalable and reliable deployments on cloud platforms like GCP and AWS.My experience includes developing a self-learning AI recruitment agent at Synapse, engineering a real-time health emergency platform at ICONFlyertech, and contributing to open-source education as part of the founding team for a Hugging Face computer vision course. My research contributions include IEEE publications on rumor detection using hybrid CNN models and a comparative analysis of object detection models for maritime surveillance.Driven by a passion for innovation, I consistently seeking to leverage the latest advancements in AI to create solutions that are not only technologically sophisticated but also deliver measurable business value.

Areas of Expertise

computer vision

Develop image recognition, object detection, video analysis, and multimodal AI systems for real-world applications.

NLP & Prompt Engineering

Build natural language processing systems, fine-tune large language models, and craft reliable production prompts for AI-powered products.

AI & Machine Learning Engineers

Design, train, and ship production ML models — from data pipelines to model inference at scale. Covers supervised learning, neural networks, LLM integration, and model optimisation.

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