AI/ML Engineer · Agentic AI Developer
Building Production-Grade AI Systems
Every aspect of learning or any other feature of intelligence can, in principle,
be so precisely described that a machine can be made to simulate it.
I'm an AI/ML Engineer based in Lahore, Pakistan, building production-grade AI Systems. My focus is on Agentic AI, Generative AI, RAG and LLM Systems, Speech AI, Computer Vision, Machine Learning & Deep Learning, MLOps and Python, from architecture to deployment and Monitoring.
I've trained LLMs, built multi-agent systems, Personal Assistants, Intelligent RAG and LLM-Powered Systems and delivered real-time CV pipelines for enterprise clients. I hold a BS in Computer Science from Sukkur IBA University and currently working as an Associate AI Engineer at Mayfair Asian Food Industries.
"Engineering intelligence that empowers humanity."
Production-grade AI systems designed for real-world impact and scalability.
Production capabilities across agentic workflows, speech AI, computer vision, and MLOps, the differentiating layer of my work.
LangChain, LangGraph, ReAct Agents, Tool-Use Agents
FAISS, Vector DBs, Hybrid Search, Contextual Retrieval
LangChain, Prompt Engineering, Function Calling, Chains
Whisper (ASR), VITS, XTTS, VAD, Speaker Embeddings
LoRA, PEFT, Hugging Face Transformers, WER Evaluation
FastAPI, Streamlit, Flask, Docker, AWS EC2/S3
MLflow, CI/CD Pipelines, GPU Inference Optimization
YOLOv9, OpenCV, CNNs, Transfer Learning, Face Recognition
Core tools I use to build and ship AI systems.
End-to-end AI engineering from strategy and architecture to production deployment. I build reliable, scalable, and business-driven AI systems that deliver measurable real-world impact.
Design and development of autonomous AI systems powered by ReAct and multi-agent architectures. Includes tool-use orchestration, Text-to-SQL agents, workflow automation, and production-grade reasoning systems.
Production retrieval systems over private data and enterprise knowledge bases, featuring hybrid search, vector indexing, contextual retrieval, and domain-specific intelligence pipelines.
Custom speech systems for multilingual and specialized domains, including ASR, TTS, voice cloning, speaker verification, and GPU-optimized transcription pipelines.
Real-time computer vision solutions for surveillance, healthcare, and industrial applications, including facial recognition, medical imaging, and large-scale object detection systems.
Task-specific adaptation of foundation models using parameter-efficient training, evaluation pipelines, dataset curation, benchmarking, and production optimization.
End-to-end AI infrastructure covering CI/CD pipelines, containerization, cloud deployment, model versioning, observability, and scalable inference services.
Industry-recognized certifications and professional training in Artificial Intelligence, Machine Learning, Large Language Models, and Software Engineering.
Hands-on AI/ML engineering across speech recognition, NLP, LLMs, and production MLOps workflows.
Intensive Python and AI training covering machine learning, deep learning, and data science fundamentals.
Core supervised learning foundations — regression, classification, and linear algebra from Andrew Ng's program.
Building production LLM applications with LangChain — chains, agents, and real-world deployment patterns.
Practical NLP with Transformers — model hub, tokenizers, and pipeline APIs via Hugging Face.
Responsible AI principles, bias mitigation, and ethical considerations in machine learning systems.
Integrating OpenAI models into applications — chat completions, embeddings, and API best practices.
Techniques for designing effective prompts to improve LLM output quality and reliability.
Accessible introduction to ML concepts, algorithms, and real-world applications for non-specialists.
Business and technical overview of AI strategy, workflow, and societal impact from Andrew Ng.
Document database fundamentals — schema design, CRUD operations, and NoSQL data modeling.
Core Python syntax, data structures, and programming fundamentals for software development.
Relational database querying — SELECT, JOINs, aggregations, and structured data retrieval.
Object-oriented programming with Java — classes, inheritance, and core language constructs.
Deep dives into the systems I've built: architecture decisions, lessons learned, and results.
How I built OfflineMail AI, a privacy-first email writer where every inference runs on-device via local LLMs, with SSE streaming and transport-layer privacy enforcement.
Read case study →A breakdown of the chunking strategy, retrieval pipeline, and the 8-step classification engine that powers the HS Code Estimation system.
Read case study →How I constrained an LLM agent to SELECT-only queries with row limits and retry caps, and why grounding beats prompting alone.
Read case study →How I built SmartInsureAI with FastAPI, MongoDB, AWS, Docker, and GitHub Actions, from data ingestion to live prediction APIs.
Read case study →Open to collaborations on Agentic AI systems, LLM applications, and production ML pipelines.
