LLM and AI API integration
Conversational experiences, content assistance, structured generation, classification, extraction, and model-backed features integrated into a real application.
AI product engineer · LLM integration · Computer vision · Automation
AI features designed to solve a product problem—not just demonstrate a model.
I connect AI models and services to reliable mobile, web, backend, and business workflows. My focus is practical AI product engineering: clear user value, dependable integrations, safe failure paths, useful human oversight, and software that can be maintained after launch.
Product-firstAI tied to a measurable user or business need
Human-awareReview, fallback, and feedback paths where appropriate
Production-readyAPIs, state, security, observability, and UX around the model
AI engineering services
Computer-vision product features
Mobile image capture, object detection, contour extraction, subject segmentation, native ML Kit and Apple Vision integration, and result-driven interfaces.
AI workflow automation
Tool and API orchestration for repetitive processes, with validation, approvals, auditability, retries, and human-in-the-loop decisions where needed.
Relevant AI product work
Lumashape
A multiplatform product that uses guided tool photography, AI object detection, and contour extraction to generate clean, accurately scaled shapes for tool-control foam design and manufacturing.
Visit product websiteSubject Segmentation CMP
A shared Android and iOS experience using Google ML Kit and Apple Vision behind a consistent Compose Multiplatform camera workflow.
View sourceAsk Me Anything
A conversational AI Android application with a clean Compose interface, coroutine-based state management, theme support, instant responses, and premium functionality.
View project catalogWhat production AI requires around the model
- Clear inputs, outputs, acceptance criteria, and user experience for uncertainty, latency, errors, and unavailable services.
- Secure API integration, controlled data flows, authentication, rate limits, retries, caching, logging, and cost-aware usage.
- Structured outputs and validation instead of treating unverified free-form model text as trusted application state.
- Evaluation using realistic product scenarios, feedback collection, human review, and a path to improve prompts, models, or workflow logic.
- Maintainable application architecture so AI is a well-defined capability rather than tightly coupled experimental code.
Questions about AI product development
Do you train foundation models?
My focus is product engineering and integration: connecting appropriate models, APIs, native ML services, and workflow logic to a reliable user experience.
Can AI be added to an existing app?
Yes. A focused feature can often be introduced behind a clean interface, measured with real users, and expanded only when it proves valuable.
How do you reduce unreliable output?
Depending on the use case: constrained inputs, structured schemas, validation, retrieval, deterministic business rules, confidence handling, and human approval.
Have an AI product idea or repetitive workflow?
Describe the current process, available data, users, decisions, and desired outcome. I can help turn it into a focused, testable, and maintainable product feature.
Email Muhammad Waqas