Competencies & Capabilities
Technical Skills
Core mathematical foundations, deep learning frameworks, and engineering methodologies.
Core Programming & Infrastructure
Foundation languages, query syntax, version control, and API development
Python
Primary language for ML modeling, PyTorch pipelines, and data preprocessing
C++
Object-oriented programming, data structures, and algorithmic complexity
SQL
Relational data querying, schema indexing, and aggregation
Git & GitHub
Branch workflows, repository maintenance, and collaborative version control
REST APIs
Endpoint consumption, client-server data streaming, and JSON integration
Deep Learning & Neural Architectures
Model design, backpropagation, and parameter-efficient optimization
PyTorch Ecosystem
Custom tensor operations, autograd, forward/loss computation
Supervised & Unsupervised Learning
Classification, regression, spatial clustering, and dimensionality reduction
Transfer Learning
Pretrained weight initialization and multi-head feature adaptation
LLM Fine-Tuning & PEFT
Supervised fine-tuning (SFT), low-rank adapters (LoRA rank 16/32, alpha 32)
Optimization & Training Protocols
AdamW, learning rate schedules, gradient clipping, cross-entropy minimization
Computer Vision & Spatial Intelligence
Pixel-level segmentation, boundary preservation, and clinical image processing
Semantic Segmentation
Encoder-decoder pipelines for pixel-level land cover delineations
Boundary Fusion & Edge Detection
Multiscale spatial boundary fusion modules for fine edge recovery
Medical & Satellite Imagery
DeepGlobe, LandCoverNet, and bone tumor diagnostic classification
Evaluation Metrics
Mean Intersection over Union (IoU), Dice Similarity Coefficient, validation loss
Natural Language Processing
Foundation model fine-tuning, autoregressive sampling, and tokenization
Language Modeling from Scratch
Custom tokenizers, embedding projections, and bigram neural architectures
Autoregressive Text Generation
Logit generation, softmax probability distributions, and temperature sampling
Qwen 2.5 Ecosystem
1.5B Instruct model fine-tuning on domain-specific prompt/response distributions
Unsloth & Hugging Face
Accelerated 2x-faster LoRA kernels with reduced VRAM footprint