Production AI systems for teams that need reliability, not demos.
I design and build agentic AI systems, RAG pipelines, and automation workflows that actually work in production — serving users, not slide decks.
Featured Case Studies
Engineering Principles
Design for Production
Every system I build is designed for real users from day one — not demos, not slide decks. That means observability, error handling, and graceful degradation are features, not afterthoughts.
Deterministic by Default
LLMs are non-deterministic. Production systems need guardrails. I wrap every AI decision point with validation layers, confidence thresholds, and fallback paths so the system behaves predictably.
Composable Architecture
Systems should be built from interchangeable, testable components. I design agent workflows, API layers, and data pipelines as modular units that can be composed, tested, and replaced independently.
Measure What Matters
If it’s not measured, it’s not production. Every system gets instrumentation for latency, accuracy, cost, and error rates. I optimize based on data, not intuition.
Current Focus
Here’s what I’m actively building and exploring.
Multi-Agent Systems
Orchestrating coordinated AI agents that decompose complex tasks across specialized roles with stateful workflows.
Production Automation
Building reliable automation pipelines that connect AI services to business workflows via n8n, Zapier, and custom middleware.
RAG at Scale
Designing retrieval-augmented generation systems that balance retrieval precision, generation quality, and production latency.
Agentic Infrastructure
Creating the tooling and patterns — guardrails, memory, observability — that make AI agents reliable enough for production.
Experience
AI & Backend Engineer
Freelance (Local & International Clients)
Designing and developing AI-powered applications, backend systems, intelligent automation solutions, and scalable APIs for clients across multiple industries.
- —Engineered AI-powered Shopify marketing automation workflows using Zapier and Klaviyo, including customer segmentation, welcome emails, abandoned cart recovery, product recommendations, and follow-up campaigns.
- —Developed backend APIs and PostgreSQL database architecture for an international Django-based e-commerce platform while collaborating with a distributed development team.
- —Built financial modeling and forecasting solutions for business reporting, revenue analysis, expense tracking, and profit/loss forecasting for North Steel.
Python Developer Intern
SoftBeck Limited
Contributed to backend development, automation solutions, and AI-powered features while collaborating with the engineering team on client-focused software projects.
- —Developed and enhanced Python-based backend solutions and automation workflows.
- —Integrated LLaMA 3 and Gemini APIs for AI-powered market research functionality.
- —Worked with REST APIs, backend service integrations, and data processing workflows.
- —Reviewed, tested, and optimized Python code to improve reliability and maintainability.
- —Collaborated with developers to implement scalable backend features and business automation solutions.
Education
Bachelor of Science in Software Engineering
National Textile University, Faisalabad
Pursuing a Bachelor's degree in Software Engineering with a strong foundation in software design, backend development, databases, cloud technologies, artificial intelligence, machine learning, and modern software engineering practices. The program emphasizes the complete software development lifecycle—from requirements analysis and system design to development, testing, deployment, and maintenance.
Tech Stack
Languages
AI Engineering
Backend Development
Databases
Automation
Frontend & Mobile
Cloud & DevOps
Data & Analytics
Engineering Notes
Designing Production RAG Systems
Lessons from building retrieval-augmented generation pipelines that actually work in production — chunking strategies, embedding selection, and evaluation.
Building Reliable AI Agents
Architectural patterns for LLM-powered agents that don't hallucinate, get stuck in loops, or fail silently in production.
FastAPI Production Patterns
Production-hardened patterns for FastAPI: dependency injection, background tasks, structured logging, and async database sessions.