monishes
Technologies:
Machine Learning / AI: Python, PyTorch, TensorFlow/Keras, Transformers, Diffusion Models, Time-Series Modeling, Computer Vision, Anomaly Detection, Open-Set Recognition, Model Evaluation, Experiment Tracking, Statistical Learning
Generative AI / LLMs: RAG, Retrieval Systems, Embedding Search, Agentic Workflows, LLM Evaluation, Prompting, OpenAI APIs, Hugging Face, LangChain-style Prototyping
Software Engineering: Python, C++, TypeScript, Kotlin, FastAPI, REST APIs, PostgreSQL, MongoDB, Data Processing Pipelines, Distributed Systems, Reproducible ML Infrastructure, Build Engineering, Containerization
Computer Vision / Scientific Computing: OpenCV, Image Registration, Motion Compensation, Segmentation, Image Reconstruction, Signal Processing, Scientific Imaging
Infrastructure / MLOps: Docker, GitHub Actions, CI/CD, Monitoring, Runtime Tracing, Performance Analysis, Cloud-Based ML Workflows
Résumé/CV: https://shorturl.at/59Qfy LinkedIn: https://www.linkedin.com/in/monish-erode-sridhar-7a1b16198/ Github: https://github.com/MonishESGit Email: [[email protected]]
M.S. Computer Science student at North Carolina State University, GPA 3.83/4.0, GSSP Fellow, with experience across machine learning, software engineering, computer vision, ML infrastructure, generative AI, scientific computing, and applied data science.
I have built large-scale ML pipelines, experiment-tracking platforms, model-evaluation systems, anomaly-detection workflows, and reproducible ML infrastructure for real-world time-series and imaging data. My work includes computer-vision pipelines for fluorescence microscopy, confidence-aware time-series classification, diffusion/transformer-based scientific imaging research, and real-time wearable-sensor ML for Samsung Galaxy Watch.
I am especially interested in roles where I can build production-grade AI systems: applied ML, AI engineering, backend/data infrastructure for ML, computer vision, LLM/RAG systems, agentic AI tools, data science, and model evaluation platforms.
Interested in: Software Engineer, Machine Learning Engineer, AI Engineer, Applied Scientist, Data Scientist, Computer Vision Engineer, Generative AI Engineer, and ML Infrastructure roles. Open to startups and larger companies.
Technologies:
Machine Learning / AI: Python, PyTorch, TensorFlow/Keras, Transformers, Diffusion Models, Time-Series Modeling, Computer Vision, Anomaly Detection, Open-Set Recognition, Model Evaluation, Experiment Tracking, Statistical Learning
Generative AI / LLMs: RAG, Retrieval Systems, Embedding Search, Agentic Workflows, LLM Evaluation, Prompting, OpenAI APIs, Hugging Face, LangChain-style Prototyping
Software Engineering: Python, C++, TypeScript, Kotlin, FastAPI, REST APIs, PostgreSQL, MongoDB, Data Processing Pipelines, Distributed Systems, Reproducible ML Infrastructure
Computer Vision / Scientific Computing: OpenCV, Image Registration, Motion Compensation, Segmentation, Image Reconstruction, Signal Processing, Scientific Imaging
Infrastructure / MLOps: Docker, GitHub Actions, CI/CD, Monitoring, Runtime Tracing, Performance Analysis, Cloud-Based ML Workflows
Résumé/CV: https://shorturl.at/59Qfy LinkedIn: https://www.linkedin.com/in/monish-erode-sridhar-7a1b16198/ Email: [[email protected]]
M.S. Computer Science student at North Carolina State University, GPA 3.83/4.0, GSSP Fellow, with experience across machine learning, software engineering, computer vision, ML infrastructure, generative AI, scientific computing, and applied data science.
I have built large-scale ML pipelines, experiment-tracking platforms, model-evaluation systems, anomaly-detection workflows, and reproducible ML infrastructure for real-world time-series and imaging data. My work includes computer-vision pipelines for fluorescence microscopy, confidence-aware time-series classification, diffusion/transformer-based scientific imaging research, and real-time wearable-sensor ML for Samsung Galaxy Watch.
I am especially interested in roles where I can build production-grade AI systems: applied ML, AI engineering, backend/data infrastructure for ML, computer vision, LLM/RAG systems, agentic AI tools, data science, and model evaluation platforms.
Interested in: Software Engineer, Machine Learning Engineer, AI Engineer, Applied Scientist, Data Scientist, Computer Vision Engineer, Generative AI Engineer, and ML Infrastructure roles. Open to startups and larger companies.
Remote: Yes
Willing to relocate: Yes
Technologies:
Machine Learning & AI — PyTorch, Transformers, Diffusion Models, Computer Vision, Time-Series Modeling, Retrieval Systems, RAG, Agentic Workflows, Model Evaluation, Experiment Tracking, Statistical Learning, Anomaly Detection
Software Engineering — Python, C++, TypeScript, Kotlin, FastAPI, REST APIs, PostgreSQL, MongoDB, Distributed Systems, Data Processing Pipelines, Reproducible ML Infrastructure
Computer Vision & Scientific Computing - OpenCV, Image Registration, Motion Compensation, Segmentation, Image Reconstruction, Signal Processing, Scientific Imaging
Infrastructure & MLOps - Docker, GitHub Actions, CI/CD, Monitoring, Performance Analysis, Cloud-Based ML Workflows
Résumé/CV: https://docs.google.com/document/d/1U7FXha5sRlFPV4VU9GScmuNc...
LinkedIn: https://www.linkedin.com/in/monish-erode-sridhar-7a1b16198/
Email: [email protected]
M.S. Computer Science, NC State University (GPA 3.83, GSSP Fellow), Thesis in Algorithms. Experience spanning machine learning, computer vision, ML infrastructure, generative AI, and scientific computing.
Previously built experiment-tracking platforms, large-scale ML pipelines, computer-vision systems for fluorescence microscopy, wearable-sensor algorithms for Samsung Galaxy Watch, and deep-learning forecasting systems for scientific imaging.
Interested in Software Engineer, Machine Learning Engineer, AI Engineer, Applied Scientist, Computer Vision, Data Science, and Generative AI roles. Open to startups and larger companies.