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.
monishes · · focus · HN ↗
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: <a href="https://shorturl.at/59Qfy" rel="nofollow">https://shorturl.at/59Qfy LinkedIn: <a href="https://www.linkedin.com/in/monish-erode-sridhar-7a1b16198/" rel="nofollow">https://www.linkedin.com/in/monish-erode-sridhar-7a1b16198/ Github: <a href="https://github.com/MonishESGit" rel="nofollow">https://github.com/MonishESGit Email: [monish2work@gmail.com]
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.