AI Product Engineering
Senior applied AI engineering across inference, computer vision, evaluation, APIs, interfaces, mobile, cloud, and deployment. We help turn models and technical concepts into usable products that can be tested, shipped, monitored, and maintained.
Engineering intelligent products that survive production
AI product work is rarely just model work. Useful systems need inference services, evaluation harnesses, data pipelines, user interfaces, deployment architecture, monitoring, and release discipline. TensorHarmony brings applied research and production engineering together across computer vision, medical imaging, generative workflows, and full-stack product delivery — with experience spanning shipped consumer AI products, clinical imaging systems, and research-to-production ML pipelines.
Production AI product delivery
End-to-end product engineering around models, workflows, and users — from inference services and APIs to web, mobile, cloud, auth, payments, and internal tooling.
- FastAPI / Python backends and inference services
- Cross-platform mobile and web applications
- Cloud-native architectures on AWS and GCP
- Auth, payments, secure delivery, and operational workflows
- Internal tools that make AI teams faster and more reliable
Computer vision & imaging systems
Detection, segmentation, classification, triage, and visualization workflows for production computer vision systems, with deep experience in CT, MRI, and medical imaging.
- Organ, lesion, vessel, and structure segmentation
- Detection and triage models for imaging workflows
- Boundary estimation and structured prediction
- Phase, sequence, and body-part classification
- Out-of-distribution and rejection logic
Evaluation & validation infrastructure
Evaluation systems built for real reviewers, technical teams, investors, clinical readers, and regulated environments — not just offline benchmark scores.
- Evaluation harnesses for model and product releases
- Reader-study- and review-aligned metric design
- Slice-, case-, and population-level analysis
- Confidence intervals, subgroup analysis, and bias checks
- Release gates for model behavior and system readiness
Generative and classical ML workflows
Generative AI, classical ML, and hybrid pipelines integrated into products with clear limits, measurable behavior, and cost-aware production paths.
- Diffusion and image-generation workflows
- Fine-tuning and domain adaptation pipelines
- Synthetic data and augmentation with explicit constraints
- Hybrid ML systems combining models, rules, and review loops
- Cost-disciplined GPU inference for shipped products
Annotation, data QA & workflow tooling
Reproducible data workflows, annotation review, curation, and QA systems that improve model reliability before training begins.
- Semi-automated curation and selection pipelines
- Annotation QA and reviewer workflow design
- Expert-review session design for domain specialists
- Annotation protocol authoring and revision
- Data versioning, lineage, and audit-ready datasets
Performance & systems integration
Low-level and systems-oriented engineering for pipelines with real memory, latency, integration, or deployment constraints.
- C++ / CUDA work for existing pipelines
- Low-memory model customization for constrained inference
- Multithreading and throughput optimization
- Integration into existing C++ or native systems
- Performance-aware deployment and inference design
Engineering principles
Design around the product, not the notebook
Modeling decisions are made with the actual deliverable in mind: a user workflow, reviewer interface, release artifact, or production service.
Make behavior measurable
Systems are built with evaluation, validation, logging, and release gates so teams can understand what changed and why it matters.
Build for operation
The goal is not a demo. It is software that can be deployed, monitored, updated, reviewed, and maintained under real constraints.
Build an AI Product That Can Ship
Discuss product architecture, inference services, evaluation, interfaces, deployment, and the engineering needed to turn a model into usable software.
Focused on deployable systems, not demos