1. Perception
Problems: segmentation and matting, monocular depth, object detection, optical flow, tracking, image-quality understanding.
Stack: PyTorch, OpenCV, image processing, data pipelines, training and validation tooling.
Technical Map
Models, runtime & evaluation
A reference to the responsibilities and tools behind selected work. Camera systems are professional experience; agent orchestration and local retrieval are independent projects.
2. Reasoning
Problems: real-time composition state machines, provider-neutral agent workflows, local retrieval, structured decisions, failure policy, human review.
Stack: Python, dataclasses, provider protocols, validated output contracts, deterministic traces, local retrieval.
3. Runtime
Problems: quantization, export compatibility, native integration, rendering, device deployment, service packaging.
Stack: TFLite, ONNX, TensorRT/ONNXRuntime, C++, Android NDK, CMake, OpenGL/GLSL, Docker, FastAPI.
4. Evaluation
Problems: hard-case definition, model replacement, visual quality, provider comparison, agent stability, release decisions.
Stack: golden cases, batch simulation, visual montages, regression suites, traces, A/B reports, acceptance gates.
5. Handoff
Problems: ambiguous requirements, cross-team dependencies, field feedback, debugging, release readiness, maintainability.
Practice: technical scoping, prototypes, issue taxonomies, playbooks, documentation, reusable workflows, production ownership.
Evidence boundary
Professional experience: computer vision, computational photography and edge deployment. Independent projects: scripted-provider orchestration, local retrieval, output validation and failure-path evaluation. Live model integration and foundation-model training are not demonstrated by this portfolio.