Professional Summary
I'm the Founder of DeepNeuro.dev, where I build applied AI systems end-to-end—from data pipelines and model training to robust APIs and cloud deployment. My recent work centers on multi-agent orchestration, RAG-powered knowledge systems, and production MLOps.
I focus on practical, production-ready engineering: FastAPI backends with typed models (Pydantic), Postgres + pgvector retrieval, Dockerized services, and frontends built with React/Next.js—deployed on Google Cloud Run, Render, and Vercel.
DeepNeuro.dev originated in healthcare AI, and I retain that domain expertise. My background in neurobiology provides a strong scientific foundation that I now apply across domains including NLP, automation, and analytics.
Education
Stanford University
Continuing Studies - A Crash Course in AI | Grade: A+
Comprehensive course covering neural networks, generative AI, and ethical considerations of AI systems. Instructor: Ronjon Nag. Covered cutting-edge AI technologies and their real-world applications.
University of California, Davis
Bachelor of Science in Neurobiology, Physiology, and Behavior | Dean's Honor List
Graduated with honors. Strong foundation in biological systems, data analysis, and scientific research methodology. Relevant coursework included Python, Calculus, Statistics, Data Analysis, Neurobiology, and Physiology. Contributed to research studies on metabolic inhibition of inflammation.
Core Expertise
Deep Learning & Neural Networks
Expert in designing and training deep neural networks for computer vision and NLP tasks. Proficient with CNNs, RNNs, Transformers, and attention mechanisms. Experience with transfer learning, fine-tuning, and model optimization techniques.
Healthcare AI
Specialized in medical imaging analysis (MRI, CT, X-ray), clinical data prediction, and bioinformatics. Familiar with DICOM standards, medical data privacy (HIPAA), and interpretable AI for clinical decision support.
Computer Vision
Developed image classification, object detection, and segmentation systems. Experience with OpenCV, image preprocessing, data augmentation, and feature engineering for visual recognition tasks.
Natural Language Processing
Built NLP pipelines using transformers, BERT, and GPT models. Experience with sentiment analysis, text classification, named entity recognition, and RAG (Retrieval-Augmented Generation) systems.
MLOps & Deployment
Skilled in deploying ML models to production using Google Cloud Platform (Vertex AI, Cloud Run), Docker, and CI/CD pipelines. Experience with model monitoring, versioning, and scalable inference systems.
Research & Innovation
Currently working on publishing research papers on various AI/ML/DL topics in healthcare and other fields. Strong research background with experience in experimental design, statistical analysis, and scientific communication. Ability to read cutting-edge papers and implement novel techniques quickly.
Technical Skills
Programming Languages
- Python (Advanced)
- R (Intermediate)
- SQL (Intermediate)
- JavaScript (Basic)
ML/DL Frameworks
- TensorFlow & Keras
- PyTorch
- scikit-learn
- XGBoost & LightGBM
- Hugging Face Transformers
Data Science Tools
- pandas & NumPy
- Matplotlib & Seaborn
- Plotly & Dash
- Jupyter Notebooks
- Apache Spark (Basic)
Specialized Libraries
- OpenCV (Computer Vision)
- NLTK & spaCy (NLP)
- Biopython (Bioinformatics)
- LangChain (LLM Apps)
- OpenAI API & RAG
Cloud & DevOps
- Google Cloud Platform
- Vertex AI
- Cloud Run & Functions
- Docker
- Git & GitHub
Domain Expertise
- Healthcare AI & Medical Imaging
- Bioinformatics & Genomics
- DICOM Image Processing
- Clinical Data Analysis
- Scientific Research Methods
What I'm Looking For
I'm actively seeking opportunities to design and deliver applied AI systems—multi-agent workflows, RAG knowledge platforms, and cloud-native AI services. I'm also open to select freelance and consulting engagements.
🎯 Primary Goal: Full-Time Employment
- AI/ML Engineer: Building and deploying production-grade AI systems
- Applied AI Systems Engineer: Agentic workflows, RAG retrieval, tool-use
- Data Scientist: Extracting insights from complex datasets and building predictive models
- Research Engineer: Implementing applied research in NLP/GenAI
- MLOps Engineer: Scaling AI applications with containerization and CI/CD
💼 Also Open To
- Freelance Projects: Short-term AI/ML consulting and development work
- Consulting Engagements: Healthcare AI strategy and implementation guidance
- Contract Positions: 3-12 month engagements with potential for full-time conversion
🔧 Technical Interests
- Agentic AI & Multi-Agent Workflows
- Natural Language Processing & Large Language Models
- Computer Vision & Object Detection
- Generative AI & RAG Systems
- MLOps & Cloud Deployment (Google Cloud Run, Render, Vercel)
- Bioinformatics & Computational Biology
- Open to learning new domains and technologies
Beyond the Code
When I'm not training models or analyzing data, I enjoy:
- 📚 Reading the latest AI research papers and implementing novel techniques
- ✍️ Writing technical blog posts to share knowledge with the community
- 🧬 Keeping up with breakthroughs in neuroscience and computational biology
- 🎓 Taking online courses to explore new areas of AI and machine learning
- 🤝 Contributing to open-source projects and collaborating with developers worldwide
I believe in the power of continuous learning and the importance of giving back to the community. Whether through blog posts, open-source contributions, or mentoring, I'm always looking for ways to share knowledge and help others on their AI/ML journey.
Let's Connect
Interested in collaborating or learning more about my work? I'm always open to discussing new opportunities and interesting projects.