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Hi, my name is Mohammed-Khalil

AI Research Associate at Meta and PhD candidate building production-grade agentic AI systems, generative AI infrastructure and end-to-end MLOps pipelines.

About me

I’m Mohammed-Khalil Ghali, an AI engineer and PhD candidate working at the intersection of agentic generative AI, machine learning, and complex industrial systems. At Meta, I build production-grade multi-agent systems that turn hardware-validation and operational workflows into fast, auditable experiences; previously, at Lattice Semiconductor, I developed cloud data products, MLOps platforms, and deep-learning applications for semiconductor manufacturing. My background spans computer science and industrial and systems engineering, shaping how I connect robust software with measurable real-world impact. Originally from Morocco and now years old, I bring a global perspective and a creative mindset to every problem. Outside work and research, you’ll usually find me playing soccer, volleyball, or racing Formula 1 cars in a simulator.

Check Out My Resume
Mohammed-Khalil Ghali

Professional Experience

Building production AI systems and data products across large scale data centers hardware infrastructure and semiconductor manufacturing.

Meta Platforms, Inc.

Contractor

AI Research Associate - Systems Integration Engineering

February 2026 – PresentMenlo Park, California, USA

  • Designed and deployed a full stack multi-agent AI engine for automated hardware test plan generation orchestrating 4+ specialized AI agents end-to-end reducing test plan creation and validation from days of engineering effort to minutes
  • Productionized an integrated multi-agent AI test cases verdict recommender as scheduled FaaS serverless jobs with a durable work queue, data persistence, and continuous self-discovering resumable batch scoring, cutting hardware repair-workflow tests reviewing from hours of manual log analysis to minutes of auditable, in-workflow recommendations
  • Created a suite of 15+ productivity AI agents skills bundled into a unified plugin to automate repetitive workflows eliminating hours of operational work and driving adoption of agentic AI as a force multiplier for our org's engineering productivity
  • Built a cross-platform agents converter enabling seamless migration of AI agents capabilities across multiple generative AI platforms and harnesses, ensuring tool portability and reuse allowing teams to adopt new AI platforms without rebuilding their agentic workflows from scratch
  • Architecting the technical roadmap of a unified AI-native platform for our org incorporating skills, plugins, agents, tools, MCP servers, and reusable no-ops deployment templates reducing time-to-production while enabling SMEs to ship production-ready AI without infrastructure expertise

Lattice Semiconductor

Contractor

AI Engineer - OneSource Data Team | Global Operations and Quality Division

December 2023 – February 2026Hillsboro, Oregon, USA

  • Participated in the development and maintenance of a cloud hosted data analytics platform handling data from more than 13 internal and external sources transformed to 100+ tables with 30B rows of ops data
  • Delivered 4 fully cloud-native quality apps to drive and encourage data-informed decision making
  • Developed a modular MLOps product (The MLPlatform) that standardizes data preparation, model training, deployment, inference, and monitoring to accelerate ML projects delivery
  • Developed and deployed end-to-end deep learning models using the MLPlatform for defect analysis presented in a web application for foundry engineers
  • Contributed to 2.99% average yield improvement across FPGA product families (ranging 0.36%-10.61%) and reduced customer returns by 141,000+ potentially failing dies through the data analytics platform
  • Supported the optimization of testing equipment utilization to support the company’s new high volume production initiative
  • Developed an agentic generative AI solution for QA customer requests, cutting ticket resolution time from 1+ month to minutes

Skills & Toolbox

A production-focused toolkit spanning AI systems, data platforms, cloud infrastructure, and software delivery.

Cloud, Infrastructure, DevOps & Monitoring

AWSAzureGCPKubernetesTupperware (Meta Internal Container Orchestration)ArgoCD/ArgoWorkflowsSandcastle (Meta Internal CI)Conveyor (Meta Internal CD)DockerTerraformGitHubJiraSentrySapling (Meta Internal Version Control)

Data & Analytics

PostgreSQLSnowflakeDatabricksTableauStreamlit

Programming

Python

ML/DL, Generative AI & MLOps

LangChainHuggingFaceTensorFlowPyTorchMLflowOpenAI Agents SDKGoogle Agents SDKAnthropic Agents SDKLangSmithMCPA2A (Google)



Research and Projects Portfolio

Projects Portfolio

Machine Learning
Generative AI
Large Language Models

A Portfolio page built for my generative AI projects.

Access the Projects Portfolio

Research and Publications

130Citations
Data Science
Generative AI

A selection of my published and active research, with full citations from my résumé.

  1. Ghali M.K, Farrag A, Won D, Jin Y, “Enhancing Knowledge Retrieval with In-Context Learning and Semantic Search through Generative AI”, Knowledge-Based Systems, 2025: 113047.

    Published

  2. Akino K, Ghali M.K, Ijuin H, Yamada T, Yoon S.W, “Demand and Capacity Sharing Decisions and Protocols in Multi-microgrid Using Deep Learning”, FAIM International Conference, 2025.

    Published

  3. Farrag A, Cao N, Ghali M.K, Jin Y, Won D, “BURGAL: An Iterative Guided Active Learning Sampling Approach for Smart Manufacturing Systems”, International Journal of Advanced Manufacturing Technologies, 2025.

    Published

  4. El Baz H, Ghali M.K, Andrus L, Won D, Yoon S.W, Wang Y, “Generative AI-based Optimization for High-Mix Warehouses: Integrating Agentic LLMs and DES”, Computers & Industrial Engineering, 2025.

    Published

  5. El Baz H, Ghali M.K, Khalid U, Pandey V, Won D, Yoon S.W, Wang Y, “A Tool-Grounded Multi-Agent LLM Workflow for Manufacturing Line Decision Support”, Computers & Industrial Engineering, 2026.

    Under review

  6. Ghali M.K, Farrag A, Sakai H, El Baz H, Lam S, Jin Y, “GAMedX: Generative AI-based Medical Entity Data Extractor using Large Language Models”, Journal of Healthcare Informatics Research, 2025.

    Under review

  7. Ghali M.K, Farrag A, Lam S, Won D, “BEYONDWORDS is All You Need: Agentic Generative AI based Social Media Themes Extractor”, Online Social Networks and Media Journal, 2025.

    Under review

  8. Ghali M.K, Molina O, Gershenson Garcia C, Won D, “Forecasting Commodity Price Shocks Using Temporal and Semantic integration of Prices Signals and Agentic Generative AI Extracted Economic News”, IEEE Transactions on Knowledge and Data Engineering, 2026.

    Under review

  9. Ghali M.K, Tamilarasan K, La T, Won D, “Predicting Static Leakage Yield Loss During Wafer Sort for Semiconductors Supply Planning using Wafer Acceptance Test Data”, International Journal of Production Research, 2026.

    Under review

  10. Ghali M.K, Kulkarni S, Kulkarni R, Kulkarni R, Yoon S.W, Won D, “Automated Hardware Validation Test Plan Generation for Large Scale AI Datacenter Platforms Using a Generative AI Multi-Agents Architecture”, IEEE Transactions on Reliability, 2026.

    Under review

  11. Jaradat H, Ghali M.K, Kulkarni S, Kulkarni R, Yoon S.W, “An LLM-Orchestrated Neuro-Symbolic Framework for Verifying Diagnostic Signal Reachability in Electronic Systems”, IEEE Transactions on Reliability, 2026.

    Under Review
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Certifications

Generative AI with Large Language Models on AWS Sagemaker

DL.AI x Coursera

Issued: October 2023.

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Build, Train, and Deploy ML Pipelines in AWS using BERT

DL.AI x Coursera

Issued: August 2023.

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Analyze Datasets and Train ML Models using AWS AutoML

DL.AI x Coursera

Issued: September 2023.

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AZ-900:Azure Fundamemtals

Microsoft

Issued: October 2023.

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Introduction to Design and Analysis of Algorithms

Binghamton University

Issued: July 2023.

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Probabiliy and Statistics for Data Science

Binghamton University

Issued: July 2023.

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Data Science Essentials Suite

Binghamton University

Issued: July 2023.

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Introduction to Python Programming

Binghamton University

Issued: July 2023.

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Introduction to Machine Learning

Binghamton University

Issued: July 2023.

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Attention Mechanism

Google Cloud

Issued: July 2023.

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Generative AI Fundamentals

DataBricks

Issued: September 2023.

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Create Image Captioning Models

Google Cloud

Issued: July 2023.

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Generative AI Fundamentals

Google Cloud

Issued: June 2023.

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Encoder-Decoder Architecture

Google Cloud

Issued: June 2023.

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Introduction to Generative AI

Google Cloud

Issued: June 2023.

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Introduction to Generative AI Studio

Google Cloud

Issued: June 2023.

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Introduction to Large Language Models (LLMs)

Google Cloud

Issued: June 2023.

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Introduction to Responsible AI

Google Cloud

Issued: June 2023.

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Transformer Models and BERT Model

Google Cloud

Issued: June 2023.

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Elements of AI Building AI

University of Helsinki

Issued: February 2022.

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Intro to AI

University of Helsinki

Issued: February 2022.

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Client Needs and Software Requirement

University of Alberta

Issued: December 2021.

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Software Processes and Agile Practices

University of Alberta

Issued: December 2021.

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CFA, Ethical Decision Making in Finance

CFA Institute

Issued: May 2021.

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Articles about Me

Mohammed-Khalil Ghali collaborating with the Endicott Police Department

Detective Work: A Better Case-Management System for Endicott Police

Systems Engineering
Software Development
Public Safety
PIMS

Binghamton University featured my collaboration with faculty, students, and the Endicott Police Department to develop and rigorously test PIMS—a digital case-management system designed to streamline investigations, improve field coordination, and save officers hundreds of hours each year.

Open Article
Album

LockerRoom345: Featured in a Binghamton University Magazine Article

Python
JS, HTML, CSS
MongoDB
API

Watson College students leverage engineering expertise to optimize clothing distribution for low-income children through LockerRoom345, in collaboration with Dick's Sporting Goods.

Open Article
Launch Website
Album

DATA4GOOD Competition: 2nd Place Nationally

Generative AI
Large Language Models (LLMS)
Named Entity Recognition
Microsoft Azure

SUNY Binghamton Students ranking 2nd nationally in the DATA4GOOD Competition organized by Purdue University, INFORMS and Microsoft. The competition is about building an NER system leveraging LLMs and Microsoft Azure to automate and streamline the process of form filling in healthcare.

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