AI Engineer | Agentic Systems | Backend AI Infrastructure | Data Engineering
I’m a BS Artificial Intelligence graduate from FAST-NUCES working at the intersection of applied AI, backend engineering, agentic workflows, and distributed systems. I build structured AI applications that connect language models with reliable software components, typed data contracts, validation layers, automated evaluation, and production-oriented APIs.
My earlier work focused primarily on scalable data infrastructure for AI systems. I worked with ETL pipelines, data extraction and transformation, large scraped datasets, distributed processing, microservices, and the data-quality systems required to support machine-learning workloads. That foundation continues to shape how I approach AI engineering: models are only one part of the system, and their value depends on the architecture, data pipelines, validation mechanisms, observability, and operational controls surrounding them.
My current work centers on designing and engineering LLM-driven agentic systems. I am particularly interested in converting ambiguous natural-language requests into structured specifications, coordinating specialized AI agents, enforcing reliable communication through Pydantic contracts, and verifying generated outputs before they reach users.
This includes work with:
- Multi-agent orchestration and stateful AI workflows
- Structured LLM outputs and schema-guided generation
- FastAPI services and asynchronous Python backends
- Pydantic models, validation boundaries, and typed agent contracts
- LangGraph-based planning, production, review, and repair workflows
- Configurable model gateways and provider-routing strategies
- Automated evaluation, failure classification, and bounded repair
- Model-response telemetry, tracing, and observability
- Browser-based verification of AI-generated applications
- Docker, CI/CD, security checks, and reproducible development environments
I’m especially interested in moving generative AI beyond isolated prompts and demonstrations. My focus is on building systems where AI-generated work can be inspected, validated, tested, repaired, and delivered through conventional software-engineering controls.
Game development is one of my strongest technical and creative interests. I enjoy exploring how AI can enrich the game-development process through procedural ideation, structured game-design generation, intelligent planning, code generation, asset coordination, automated testing, and adaptive interactive experiences.
I currently experiment with integrating agentic AI workflows into 2D browser-game development, using technologies such as TypeScript, Phaser, Vite, and browser automation. The larger goal is to understand how specialized AI agents can collaborate across game design, gameplay engineering, scripting, UI/UX, level pacing, assets, sound, quality assurance, and technical verification—while still producing editable and maintainable software.
My engineering background also includes:
- ETL pipeline design, optimization, and failure handling
- Web scraping, data transformation, and dataset curation
- Data validation, governance, and quality assurance
- Microservice-based application architecture
- REST and gRPC service communication
- Distributed and federated machine-learning workflows
- Containerized development with Docker
- CI/CD pipelines and automated quality checks
- Backend systems for machine-learning and NLP applications
- Research-oriented experimentation with large and noisy datasets
I am interested in architectures that combine AI models with dependable data infrastructure, distributed execution, traceable system behavior, and clearly defined operational boundaries.
My recent academic and applied research spans:
- Privacy leakage in public blockchains under AI-augmented analysis
- Federated intrusion detection for connected vehicles
- Low-light image enhancement and classification
- Retrieval-grounded multi-agent reasoning
- Knowledge drift, self-consistency, and evidence alignment
- Urdu word-sense disambiguation using multilingual transformers
- Post-quantum cryptography and quantum-resistant applications
Languages: Python, TypeScript, JavaScript, C++, C#, Java AI and ML: PyTorch, TensorFlow, Scikit-learn, Transformers, RAG, LLM applications Agentic Systems: LangGraph, structured prompting, multi-agent workflows, model gateways Backend: FastAPI, Flask, Pydantic, REST APIs, gRPC Data Engineering: Pandas, NumPy, ETL pipelines, web scraping, data validation Game and Web: Phaser, Vite, browser automation, Playwright Engineering: Docker, GitHub Actions, CI/CD, structured logging, automated testing Research Tools: Flower, SUMO, Veins, NLP and computer-vision experimentation
I want to contribute to teams building reliable AI products, agentic applications, intelligent developer tools, AI-enabled interactive systems, and scalable backend infrastructure. I am particularly drawn to work that combines applied machine learning with rigorous software engineering—where systems must do more than generate plausible output; they must remain observable, testable, maintainable, and useful.