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I am a Master's candidate in Computer Science specializing in Machine Learning & AI, with hands-on experience in NLP, computer vision, and healthcare AI. I enjoy turning research into deployed ML systems using Python, PyTorch, and cloud platforms such as AWS and GCP. Through roles as a graduate researcher and software engineer intern, I have benchmarked LLMs on clinical queries, built medical image synthesis pipelines, and streamlined ML deployment pipelines for scalable services. I’m passionate about developing robust AI solutions that assist clinicians and improve patient care.…I am a Master's candidate in Computer Science specializing in Machine Learning & AI, with hands-on experience in NLP, computer vision, and healthcare AI. I enjoy turning research into deployed ML systems using Python, PyTorch, and cloud platforms such as AWS and GCP. Through roles as a graduate researcher and software engineer intern, I have benchmarked LLMs on clinical queries, built medical image synthesis pipelines, and streamlined ML deployment pipelines for scalable services. I’m passionate about developing robust AI solutions that assist clinicians and improve patient care.

Amrutha Kanakatte Ravishankar

AI Engineer, Data Scientist, Full Stack Developer, +2

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I am a Master's candidate in Computer Science specializing in Machine Learning & AI, with hands-on experience in NLP, computer vision, and healthcare AI. I enjoy turning research into deployed ML systems using Python, PyTorch, and cloud platforms such as AWS and GCP. Through roles as a graduate researcher and software engineer intern, I have benchmarked LLMs on clinical queries, built medical image synthesis pipelines, and streamlined ML deployment pipelines for scalable services. I’m passionate about developing robust AI solutions that assist clinicians and improve patient care.…I am a Master's candidate in Computer Science specializing in Machine Learning & AI, with hands-on experience in NLP, computer vision, and healthcare AI. I enjoy turning research into deployed ML systems using Python, PyTorch, and cloud platforms such as AWS and GCP. Through roles as a graduate researcher and software engineer intern, I have benchmarked LLMs on clinical queries, built medical image synthesis pipelines, and streamlined ML deployment pipelines for scalable services. I’m passionate about developing robust AI solutions that assist clinicians and improve patient care.

Available to hire

I am a Master’s candidate in Computer Science specializing in Machine Learning & AI, with hands-on experience in NLP, computer vision, and healthcare AI. I enjoy turning research into deployed ML systems using Python, PyTorch, and cloud platforms such as AWS and GCP.

Through roles as a graduate researcher and software engineer intern, I have benchmarked LLMs on clinical queries, built medical image synthesis pipelines, and streamlined ML deployment pipelines for scalable services. I’m passionate about developing robust AI solutions that assist clinicians and improve patient care.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Language

English

Fluent

Work Experience

Graduate Research Assistant (Part-time) - Machine Learning & NLP at Stevens Institute of Technology

May 1, 2025 - Present

Benchmarked 5 LLMs (GPT-5, GPT-4, Qwen3, GLM-4.5, Grok-2) on 12,000+ clinical queries using the MedESQ framework; analyzed NoSQL retrieval performance across complexity types to improve QA systems. Implemented ReAct prompting framework to evaluate 6 LLMs on complex clinical queries, achieving 114% total accuracy improvement (35%→75%) on difficult case analyses through enhanced reasoning chains. Analyzed complexity-driven taxonomy and evaluation methodologies for NoSQL healthcare systems to inform robust AI system design for clinical decision support.

AI Research Engineer Intern - Deep Learning & Computer Vision at PONS

June 1, 2025 - October 1, 2025

Developed a Pix2Pix GAN pipeline for medical image synthesis using large-scale real-world datasets to address data scarcity and generate high-fidelity training data for downstream models. Optimized generative model architectures using PyTorch, improving output quality by 18% and reducing computational overhead by 25% through hyperparameter tuning and model compression. Architected a novel hybridGAN with Spatially Adaptive Denormalization for segmentation in medical images, enhancing precision for identifying anatomical structures.

Software Engineer Intern - Backend Systems & MLOps at FutureWave

August 1, 2021 - May 1, 2022

Built automated CI/CD pipelines for AI model deployment serving 50,000+ users, achieving 99.5% uptime and 20% improved system reliability through containerized deployments and monitoring. Optimized backend ML pipelines for data preprocessing and model inference with Python and FastAPI, reducing serving latency by 35% and increasing throughput by 15%. Managed end-to-end ML deployment lifecycle on AWS using Docker and Kubernetes, with automated testing and rollback procedures for zero-downtime deployments.