Applied Machine Learning Engineer
NLP, multimodal computer vision, reinforcement learning, and MLOps
I build research-driven machine learning systems with an emphasis on reproducibility, evaluation quality, and repository clarity.
I focus on building ML projects that are technically solid and easy to evaluate: clear problem definition, structured training pipelines, measurable results, and documentation that helps other engineers review the work quickly.
My recent work spans:
- sarcasm and sentiment classification across English varieties using encoder, decoder, and adapter-based NLP pipelines
- multimodal wildfire burned-area prediction from pre-fire satellite, terrain, weather, and infrastructure inputs
- robust locomotion transfer in MuJoCo with PPO, curriculum domain randomization, and entropy scheduling
- local MLOps infrastructure using Kubernetes, Istio, Katib, and browser-based development workflows
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Unified experimentation repo for sarcasm and sentiment classification across multiple model families and English varieties. Stack: Python, Transformers, RoBERTa, DistilBERT, Mistral, QLoRA, VAAT Highlights: config-first pipelines, custom heads, adapter tuning, evaluation, plotting, and error analysis on a |
Multimodal geospatial computer vision project for predicting final burned area from pre-fire observations. Stack: Python, PyTorch, remote sensing, EfficientNet, FPN Results: best multimodal model reached |
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MuJoCo Hopper transfer-learning research on robust locomotion under dynamics shift. Stack: Python, MuJoCo, PPO, robust RL, evaluation tooling Results: |
Laptop MLOps lab that provisions a private-cloud style ML workflow on top of a local Kubernetes cluster. Stack: Python, Kubernetes, kind, MetalLB, Istio, Katib, Docker Highlights: end-to-end local platform setup with deployment runbooks, architecture docs, browser IDE access, and experiment infrastructure. |
- end-to-end ML experimentation: data handling, training, evaluation, diagnostics, and reporting
- research translation: converting academic ideas into reproducible implementations
- repository quality: readable project structure, documentation, and evidence-backed results
- technical range: NLP, computer vision, reinforcement learning, geospatial ML, and ML systems
| Area | Tools |
|---|---|
| ML & AI | Python, PyTorch, Transformers, QLoRA, Scikit-learn, Jupyter |
| Domains | NLP, Computer Vision, Remote Sensing, Multimodal Learning, Reinforcement Learning |
| Systems | Kubernetes, kind, Istio, Katib, Docker, Git |
| Frontend | Vue 3, Pinia, Tailwind CSS |
- AI/ML internship
- Junior machine learning engineer
- Applied machine learning engineer
- Research engineering or research assistant role
If you are reviewing my profile for hiring, these are the best entry points:
- sarcasm-detection-nlp for NLP experimentation and model comparison
- WildFire for multimodal computer vision and geospatial ML
- RL for reinforcement learning, robustness, and transfer learning
- ML_Ops for infrastructure thinking and MLOps workflow design
I am most interested in teams where strong implementation, clear documentation, and measurable ML results matter as much as model choice.