Chandra Prakash

Co-Founder

1 yr 2 mos experience

AI EnabledAI ML Practitioner

Key Highlights

Built machine learning infrastructure from scratch.
Achieved over 90% accuracy in predictive modeling.
Contributed to advanced generative modeling techniques.

Stackforce AI infers this person is a Healthcare-focused Machine Learning Engineer with expertise in NLP and generative modeling.

Contact

Skills

Core Skills

Machine LearningNatural Language Processing (nlp)Generative AiComputer Vision

Other Skills

Deep LearningLarge Language Models (LLM)Model CompressionRetrieval-Augmented Generation (RAG)Fine TuningTransformer ModelsPython (Programming Language)QuantizationLatent Diffusion ModelsDiffusion TransformersPhysics Informed Neural NetworksGenerative Adversarial Networks (GANs)Multi-agent SystemsStable DiffusionGenerative Modeling

About

Focused on advancing machine learning, with a particular interest in optimizing LLMs and enhancing their reasoning, alignment, efficiency, and accuracy.

Experience

1 yr 2 mos

Total Experience

1 yr 2 mos

Average Tenure

Current Experience

Zynix.ai

Founding Machine Learning Engineer

May 2024 – Jul 2025 · 1 yr 2 mos · Seattle, Washington, United States · Remote

Joined as the first ML Engineer and built the company’s machine learning infrastructure from scratch.
Developed a Transformer-based model on raw EMR data to predict readmission, mortality, and clinical risks with over 90% accuracy.
Built Voice AI agents for scheduling and follow-ups, integrated with a custom MCP Server to maintain patient context and update hospital/ACO systems.
Designed RAG pipelines on EMR and claims data, achieving over 75% accuracy in HCC suspect coding to support value-based care.
Automated SOAP note generation from audio transcripts, improving information recall by 40%.
Fine-tuned LLMs on proprietary healthcare data to enhance clinical NLP performance.
Built a text-to-SQL engine with MCP Server integration for context-aware querying across healthcare databases.

Deep LearningNatural Language Processing (NLP)Large Language Models (LLM)Generative AIModel CompressionRetrieval-Augmented Generation (RAG)+4

Uit- the arctic university of norway

Research Intern

Apr 2024 – Nov 2024 · 7 mos · Norway · Remote

Conducted research on LLM quantization, focusing on understanding the fundamental changes in quantized models compared to normal ones, with the goal of improving the accuracy and reliability of current quantization methods.

Large Language Models (LLM)QuantizationMachine Learning

Flamel

Research Intern

Feb 2024 – Apr 2024 · 2 mos · Bengaluru, Karnataka, India · Remote

Conducted research and development on advanced generative modeling techniques, leveraging state-of-the-art latent diffusion models and diffusion transformers to address complex image synthesis challenges.

Generative AILatent Diffusion ModelsDiffusion Transformers

Indian institute of science (iisc)

Research Intern

May 2023 – Jul 2023 · 2 mos · Bengaluru, Karnataka, India · On-site

Worked on learning solutions to highly non-linear PDEs using Physics Informed Neural Networks integrated with Generative Adversarial Networks. Translated research into code, trained models, and contributed to experiments.

Physics Informed Neural NetworksGenerative Adversarial Networks (GANs)Machine Learning

Indian institute of technology (indian school of mines), dhanbad

Research Intern and Mentor

Nov 2022 – Apr 2024 · 1 yr 5 mos · On-site

Worked on research projects in Computer Vision, Generative AI, and Model Compression under the guidance of faculty members. Contributed to model development, experimentation, and benchmarking. Also served as a founding member and Machine Learning Mentor at the AI Club, guiding students and fostering a strong ML Research community.

Computer VisionGenerative AIModel Compression