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AI Engineering · Applied Research · Production Systems

Abuzar Khan

AI Systems Engineer

Building end-to-end AI systems for real-world applications

I build production AI systems using LLMs, agentic workflows, computer vision, predictive machine learning, and scalable data pipelines, from experimentation and evaluation to APIs and cloud deployment.

LLM Applications · AI Agents · Computer Vision · MLOps · Data Engineering

Based in Islamabad, Pakistan · Open to AI engineering and research opportunities

Overview

Professional Highlights

Engineering and research experience across the complete AI lifecycle, from data preparation and model development to deployment and production integration.

  • End-to-End AI Systems

    Experience developing AI workflows across data ingestion, preprocessing, model development, evaluation, APIs, automation, deployment, and monitoring.

    Full AI lifecycle

  • LLM & Agentic Workflows

    Building retrieval, document intelligence, conversational AI, structured-output, tool-using, and workflow-automation systems.

    Retrieval · Agents · Automation

  • Vision & Predictive Intelligence

    Applied work involving deepfake detection, image forensics, object detection, predictive maintenance, anomaly detection, and time-series learning.

    Vision · Predictive ML

  • Research to Production

    Combining research-driven experimentation with production-oriented engineering practices for scalable and reliable AI applications.

    Published AI research

What I build

Capabilities

I build AI systems across the full stack, from models and intelligent applications to APIs, automation, and deployment.

  • RAG and document intelligence
  • Conversational AI and semantic search
  • Tool-using agents and workflow automation
  • Structured outputs, evaluation, and guardrails
  • Python
  • LangChain
  • FastAPI
  • Transformers
  • Object detection, segmentation, and image forensics
  • Predictive maintenance and time-series forecasting
  • Fault detection and anomaly detection
  • Model optimization, explainability, and real-time inference
  • PyTorch
  • TensorFlow
  • OpenCV
  • Scikit-learn
  • ETL, data ingestion, validation, and transformation
  • Model-serving APIs and application integration
  • Cloud deployment and workflow orchestration
  • Monitoring, experiment tracking, and MLOps
  • FastAPI
  • Docker
  • Airflow
  • AWS
  • SQL

Selected Work

Selected AI Systems

Selected work across LLM applications, agentic workflows, computer vision, predictive machine learning, and production AI infrastructure.

FeaturedProduction-oriented prototype

LLM and Agentic AI

Enterprise Document Intelligence

Problem

Contracts and invoices arrive as unstructured documents that teams must read manually before any downstream system can use them.

System

An AI document-processing system that ingests documents, applies OCR and parsing, chunks and embeds the text, and uses retrieval with LLM orchestration to produce entity extractions and structured reports. Results are exposed through a FastAPI service for application integration.

My contribution

Designed the document-processing workflow, built the ingestion and preprocessing pipeline, implemented retrieval and LLM orchestration, developed structured API outputs, and created evaluation and error-analysis workflows.

Case study: Enterprise Document IntelligenceGitHub repository for Enterprise Document Intelligence

System flow

  1. Documents
  2. Parsing and OCR
  3. Chunking
  4. Embeddings
  5. Retrieval
  6. LLM processing
  7. Structured output
  8. API

Technical focus

  • Retrieval-augmented generation
  • OCR and text parsing
  • Entity extraction
  • Structured outputs
  • Workflow automation

Stack

  • Python
  • LangChain
  • Transformer models
  • FastAPI
  • REST APIs

Also involves

  • Retrieval-Augmented Generation
  • Document Data Pipelines
FeaturedAcademic project

Computer Vision

Explainable Deepfake and Image Forgery Detection

Problem

Manipulated images and deepfakes are difficult to judge from a prediction alone, so a detector must also show why it flagged an image.

System

An explainable computer-vision system that detects manipulated images using deep learning and vision transformers, then produces attention-based explanation maps to localise the suspected manipulation. The work spans the DEEPFORGE final-year project and the related ViT-Xplain research on explainable detection.

My contribution

Built the image preprocessing pipeline, trained and evaluated the vision models, implemented attention visualisation for forgery localisation, and ran the error analysis used to compare model variants.

Case study: Explainable Deepfake and Image Forgery Detection

System flow

  1. Image input
  2. Preprocessing
  3. Vision model
  4. Manipulation prediction
  5. Attention or explanation map
  6. Forensic output

Technical focus

  • Deepfake detection
  • Forgery localisation
  • Vision transformers
  • Attention visualisation
  • Explainable AI

Stack

  • PyTorch
  • OpenCV
  • NumPy
  • Python

Also involves

  • Research Projects
  • Explainable AI
Research prototype

Computer Vision

Autonomous Vehicle Perception System

Problem

Autonomous-driving perception requires a unified spatial understanding of the scene from several camera and sensor streams.

System

A multi-camera perception prototype that preprocesses synchronised sensor streams, extracts features, builds a bird's-eye-view representation with BEVFormer, and performs 3D object detection and tracking. Built as a research prototype for autonomous-driving perception, not a vehicle deployment.

My contribution

Built the sensor data pipeline and preprocessing stage, integrated the BEV detection model, and set up the evaluation workflow used to compare detection and tracking configurations.

  1. Camera and sensor data
  2. Preprocessing
  3. Feature extraction
  4. BEV representation
  5. 3D detection
  6. Tracking and output

Technical focus

  • Multi-camera processing
  • 3D object detection
  • Sensor fusion
  • Object tracking
  • Real-time inference

Stack

  • PyTorch
  • BEVFormer
  • OpenCV
  • Python
Case study: Autonomous Vehicle Perception SystemGitHub repository for Autonomous Vehicle Perception System
Published research implementation

Predictive Machine Learning

Predictive Maintenance and Fault Detection

Problem

Industrial equipment failures surface late, when sensor drift has already turned into downtime.

System

A predictive-learning system that validates and preprocesses sensor time series, engineers temporal features, and applies residual and CRNN-style models to produce calibrated early-fault risk scores that feed maintenance alerts. It implements the direction of my published work on early-warning industrial fault detection.

My contribution

Designed the time-series preprocessing and feature pipeline, implemented and calibrated the fault-detection models, and built the evaluation pipeline used to report early-warning performance.

  1. Sensor data
  2. Validation and preprocessing
  3. Feature pipeline
  4. Time-series model
  5. Risk score
  6. Alert or maintenance recommendation

Technical focus

  • Predictive maintenance
  • Fault detection
  • Model calibration
  • Imbalanced learning
  • Anomaly detection

Stack

  • PyTorch
  • Scikit-learn
  • Pandas
  • NumPy
Case study: Predictive Maintenance and Fault DetectionPublication related to Predictive Maintenance and Fault Detection
Client project

LLM and Agentic AI

Multilingual Learning Assistant

Problem

Lecture recordings are hard to study from when learners do not share the language of the recording.

System

A multilingual learning workflow that extracts audio from lecture videos, transcribes it with speech recognition, translates and indexes the transcript, and then answers learner questions and generates quizzes from the indexed content. Built during my TLS work on learning-content automation.

My contribution

Built the transcript and translation pipeline, implemented the retrieval and question-answering workflow, and automated quiz generation from processed lecture content.

  1. Lecture video
  2. Audio extraction
  3. Speech recognition
  4. Transcript processing
  5. Translation and indexing
  6. Q&A and quiz generation

Technical focus

  • Speech recognition
  • Translation and multilingual NLP
  • Retrieval and question answering
  • Quiz generation
  • Workflow automation

Stack

  • Python
  • Transformer models
  • LangChain
  • FastAPI
Case study: Multilingual Learning Assistant
Production-oriented prototype

Predictive Machine Learning

Fraud and Anomaly Detection

Problem

Suspicious transactions are rare and shifting, so rule-based monitoring misses new fraud patterns.

System

A transaction-monitoring workflow that preprocesses transaction data, engineers behavioural features, and combines supervised ensemble models with anomaly detection under strong class imbalance. Scoring is exposed through an inference API for integration into application workflows.

My contribution

Built the transaction preprocessing and feature-engineering pipeline, trained and evaluated the ensemble and anomaly-detection models, and implemented the inference API.

  1. Transaction data
  2. Preprocessing
  3. Feature engineering
  4. Ensemble and anomaly models
  5. Risk score
  6. Inference API

Technical focus

  • Feature engineering
  • Ensemble learning
  • Anomaly detection
  • Imbalanced data handling
  • Inference APIs

Stack

  • Scikit-learn
  • XGBoost
  • Pandas
  • FastAPI
Case study: Fraud and Anomaly Detection
View all projects

Experience

Professional Experience

Experience building and researching AI systems across data preparation, machine learning, LLM applications, automation, and production integration.

Data FoundationsAI ResearchApplied AI EngineeringProduction AI Systems

  1. Production AI Systems

    AI Engineer

    Zamu AIRemote

    Current

    Developing production-oriented AI services, data pipelines, and intelligent workflows for real-world applications.

    • Design and develop AI services combining data processing, model or LLM inference, workflow automation, and application APIs.
    • Build scalable machine-learning and ETL pipelines for data ingestion, preprocessing, training, evaluation, and inference.
    • Develop LLM-powered workflows involving retrieval, structured outputs, external tools, and production-system integration.
    • Improve system reliability through evaluation datasets, error analysis, model optimization, and deployment testing.
    • Collaborate with engineering and product teams to integrate AI capabilities into production applications.
    • LLM Systems
    • Agentic Workflows
    • ETL Pipelines
    • FastAPI
    • Model Evaluation
    • Deployment

  2. Applied AI Engineering

    AI Engineer

    Upwork, TLS (Think-Learn-Speak)Remote

    Built a multilingual AI learning workflow for processing lecture content, answering questions, and generating educational material.

    • Developed a lecture-processing pipeline covering audio extraction, speech recognition, transcript preprocessing, and multilingual translation.
    • Built an AI-powered question-answering assistant using lecture transcripts, NLP, retrieval, and LLM workflows.
    • Automated quiz and learning-content generation from structured lecture transcripts.
    • Designed preprocessing and dataset-transformation pipelines supporting model and application workflows.
    • Integrated Python-based AI components into an end-to-end educational application.
    • Speech Recognition
    • NLP
    • Retrieval
    • LLMs
    • Python
    • Automation

  3. AI Research

    AI Researcher

    National Centre of Artificial Intelligence (NCAI)Peshawar, Pakistan

    Conducted applied AI research across computer vision, predictive machine learning, LLM systems, and intelligent-data applications.

    • Developed and evaluated AI research systems using PyTorch, TensorFlow, Keras, and scikit-learn.
    • Built research prototypes involving computer vision, predictive maintenance, LLMs, RAG, federated learning, and intelligent transportation systems.
    • Improved model performance through hyperparameter tuning, feature engineering, imbalance-aware learning, model compression, and error analysis.
    • Designed pipelines for collecting, cleaning, validating, annotating, and transforming research datasets.
    • Contributed to experiment design, technical documentation, result analysis, and research-publication workflows.
    • PyTorch
    • TensorFlow
    • Computer Vision
    • Predictive ML
    • RAG
    • Federated Learning
    • Research
    Selected research

  4. Data Foundations

    Data Collector Intern

    National Centre of Artificial Intelligence (NCAI)Peshawar, Pakistan

    Supported machine-learning research through field data collection, dataset preparation, annotation, and quality validation.

    • Collected and curated domain-specific datasets for machine-learning research.
    • Conducted field visits and documented real-world data conditions and collection requirements.
    • Cleaned, annotated, validated, and transformed raw data into machine-learning-ready datasets.
    • Performed dataset-quality checks and supported exploratory model-development activities.
    • Maintained structured records for data provenance, annotation, and validation where applicable.
    • Data Collection
    • Annotation
    • Validation
    • Data Cleaning
    • Dataset Preparation

Research

Selected Research

Applied AI research across explainable computer vision, predictive maintenance, privacy-preserving learning, multi-agent systems, and intelligent infrastructure.

Featured research

ViT-Xplain: A Transparent Deepfake Detector for Consumer Electronics Based on Attention and Explainable AI

IEEE Transactions on Consumer Electronics
Published
Computer Vision and Explainable AI

An explainable deepfake-detection framework using vision-transformer representations and attention-based interpretation to improve transparency in manipulated-media classification.

  • Deepfake detection
  • Vision transformers
  • Explainable AI
  • Attention analysis

Contribution: Model development, explainability pipeline, and evaluation

View paperRelated project

Featured research

Smart Predictive Maintenance: A TCN-Based System for Early Fault Detection in Industrial Machinery

Machines
Published
Predictive Maintenance and Time-Series Learning

A temporal-convolutional approach for identifying early fault patterns in industrial-machinery sensor data.

  • Industrial AI
  • Temporal convolutional networks
  • Fault detection
  • Sensor analytics

Contribution: Model development, experimental design, and evaluation

View paperRelated project

Engine Failure Prediction on Large-Scale CMAPSS Data Using Hybrid Feature Selection and Imbalance-Aware Learning

CMC
Published
Predictive Maintenance and Time-Series Learning

A predictive-maintenance approach combining feature-selection methods and imbalance-aware learning for early engine-failure prediction on large-scale sensor data.

Contribution: Feature engineering, model development, and result analysis

View paper

Secure and Differentially Private Edge-Cloud Federated Learning Framework for Privacy-Preserving Maritime AIS Intelligence

CMC
Published
Federated and Privacy-Preserving AI

An edge-cloud federated-learning framework designed to support collaborative maritime intelligence while protecting distributed data.

Contribution: System architecture, experimental design, and evaluation

DOI

Evaluating Routing Stability and Coordination in Swarm-Based Multi-Agent Task-Oriented Dialogue Systems

Scientific Reports
Published
Multi-Agent and Intelligent Systems

An evaluation of routing stability, task coordination, and communication behavior in swarm-based multi-agent dialogue systems.

Contribution: Experimental design, evaluation, and result analysis

View paper
View all publications (16)

Background

Education & Certifications

Formal training and certifications supporting my AI systems engineering practice

Master of Science in Computer Science

CECOS University Peshawar

Specialization: Artificial Intelligence

September 2025 – Present

Bachelor of Science in Software Engineering

CECOS University Peshawar

CGPA: 3.22/4.00
April 2020 – July 2024

Final Year Project: DEEPFORGE

Forensic Analysis of RGB Images Using Deep Learning - Developed a deep learning-based system to detect forged or tampered regions in RGB images, improving accuracy in identifying manipulated items for forensic analysis.

Semester Project: CITIZEN SPHERE

An innovative application for the citizens of Pakistan, aimed at digitalizing National Identity Cards and providing access to various government services, improving efficiency, transparency, and convenience.

Professional Certifications

✓Cloud Computing Fundamentals - Google✓Networking & Security in Google Cloud - Google✓Data, ML, and AI in Google Cloud - Google✓Regression and Classification - Deeplearning.AI✓Introduction to Vertex AI Studio - Google✓Python for Data Science and AI - Coursera✓Transformer Models and BERT Model - Google

Contact

Get In Touch

Open to AI systems engineering roles, applied AI consulting, and research collaboration

Contact Information

Reach out about production AI systems, LLM and agentic applications, computer vision, or research collaboration.

Email

Abuzarkhaan580@gmail.com

Location

Islamabad, Pakistan

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