CV

Downloadable CV and structured resume.

Contact Information

Name Muhammad Ammar
Professional Title AI Research at Siemens | MSc Electromobility-ACES @ FAU
Email muhammad.ammar@fau.de
Location Erlangen, Bavaria
Website https://ammar2k.github.io

Professional Summary

Machine Learning engineer working on LLM pipelines, knowledge graphs, ontology-driven graph-based analytics, and applied AI. Pursuing an MSc in Electromobility-ACES at Friedrich-Alexander-Universitat Erlangen-Nurnberg while researching GenAI-enabled software ecosystem health monitoring solutions at Siemens.

Experience

  • 2026 - Present

    Erlangen

    Master's Thesis
    Siemens AG + FAPS FAU
    Beyond the Agent: A Workflow-Level Review of Agentic AI Systems.
    • Research objective: How are agentic AI systems structured, controlled, and evaluated as end-to-end workflows, and where do current approaches lack system-level rigor?
  • 2024 - Present

    Erlangen

    Data Analytics Researcher - Working Student
    Siemens AG
    Research on software ecosystem health monitoring, ontology-driven ETL, and LLM-based knowledge graph analytics.
    • Conducted a systematic literature review of software ecosystem health analysis, surveying 61 research articles and 416 health metrics to build a unified metric ontology for ecosystem health monitoring.
    • Designed ontology-driven ETL pipelines that transform research metrics, KPIs, and platform ecosystem data into structured Neo4j knowledge graphs.
    • Developed an LLM-based Text-to-Cypher pipeline that maps natural-language prompts to knowledge graph queries for software ecosystem health analytics.
    • Modeled software ecosystem health as a metric knowledge graph, connecting indicators, KPI trees, and graph algorithms for real-time analysis and visualization.
    • Developed an LLM-powered Prompt-to-Visualization pipeline to display the most relevant dashboard for a user query, lowering TTI from 2 minutes to 5 seconds.
    • Submitted 6 invention disclosures and authored 4 research papers.
  • 2025 - 2026

    Erlangen

    Project Thesis
    FAPS FAU
    Architectural Paradigms of Multi-Agent Systems and Agentic AI in Intelligent Manufacturing Systems, A Systematic Literature Review.
    • Research objective: How have agent architectures evolved in manufacturing systems, and what architectural design principles emerge from this evolution that are relevant for manufacturing control?
  • 2024 - 2024

    Remote

    Machine Learning Engineer
    NeuroOceans AI
    Built and evaluated audiovisual deepfake detection models.
    • Finetuned multiple audiovisual deepfake detection models and evaluated them on performance metrics.
    • Led the entire pipeline development, from dataset collection and preprocessing to model training and evaluation.
  • 2023 - 2023

    Remote

    Solution Analyst
    Learners.ai
    Built GPT-powered workflow automations and chatbots using LangChain and Pinecone.
    • Used LangChain and Pinecone to create a GPT-powered RAG chatbot that takes a sales call transcript and generates a sales proposal.
    • Developed a copywriter chatbot using LangChain Agents, tool calling, and a curated knowledge base.
    • Conducted Data Import Analysis for data transfer across CRMs, using Excel, SQL, and Python.
    • Created ad campaign effectiveness dashboards in HubSpot and Databox.
  • 2021 - 2022

    Islamabad, Pakistan

    Undergraduate Researcher
    Robotics and Intelligent Systems Engineering (RISE) Laboratory
    Senior year research project focused on mobile robotics.
    • Engaged in a senior year research project, providing bi-weekly presentations to a supervisory panel.
    • Developed a control algorithm for a mobile robot using Arduino, ultrasonic sensors, IR sensors, and encoders.
    • Successfully secured funding through IGNITE National Grassroots Research Initiative 2022 and obtained funding for AHFE Conference registration fees under the Pakistan and European Unions Horizon 2020 ENHANCE project.

Education

  • 2024 - Present

    Erlangen, Germany

    MSc
    Friedrich-Alexander-Universitat Erlangen-Nurnberg (FAU)
    Electromobility-ACES
    • Current grade: 1.4
  • 2025 - 2025

    Pisa, Italy

    Seasonal School
    Scuola Superiore Sant'Anna (SSSA)
    AI & Robotics in Extended Reality Seasonal School
  • 2018 - 2022

    Islamabad, Pakistan

    B.Sc.
    National University of Sciences and Technology (NUST)
    Mechanical Engineering

Publications

Skills

AI and Machine Learning (Advanced): Python, PyTorch, LLMs, Agentic AI, Multi-Agent Systems, Prompt Engineering, Natural Language Processing, Computer Vision, Machine Learning, Data Science
Knowledge Graphs and Data (Advanced): Knowledge Graphs, Ontologies, Ontology-Driven ETL, ETL Pipelines, Graph Analytics, Neo4j, Cypher, LangChain
Robotics (Intermediate): ROS2, Arduino, Controls

Languages

English : C2, IELTS 8.5

Certificates

Projects

  • Autonomous Mobile Robot

    Designed and developed an autonomous mobile robot for warehouse operations, including obstacle avoidance and an attachable live-roller mechanism for automated loading and unloading.

    • Robotics
    • Arduino Programming
    • Controls
    • Design
  • Image2Audio app

    Users upload an image, the app generates a caption for the image, creates a short story based on the caption, and then converts the story into audio format.

    • OpenAI API
    • HuggingFace
    • LangChain
    • Streamlit
    • TTS
  • Q&A Chatbot on private data

    Blog post on Retrieval Augmented Generation (RAG) using ChromaDB, GPT wrapped around LangChain, and private data.

    • OpenAI API
    • LangChain
    • Vector Database
    • Blogpost
  • Scene Classification

    Finetuned ViT pre-trained on ImageNet for Kaggle Intel Classification Challenge.

    • PyTorch
    • Computer Vision
    • Gradio
  • Podcast Summarizer

    Converts podcast audio to text using Whisper, uses GPT-3.5 to generate a summary, and uses the Wikipedia API to display additional information.

    • OpenAI API
    • Whisper
    • Audio2Text
    • Streamlit
  • Transfer Learning with PyTorch

    Scene classification by fine-tuning pre-trained Vision Transformers (ViT) guide.

    • PyTorch
    • Vision Transformer
    • Blogpost