AI-Focused Software Engineer | Java · Python · Spring Boot · AWS | Master's in CS (AI) @ IWU
Contact MeHello! I am Mohammed Asaad, a Software Engineer with 7+ years of experience — and now bringing that foundation into AI.
I'm currently completing my Master's in Computer Science at Indiana Wesleyan University (GPA: 4.0), with a focus on Artificial Intelligence, Big Data, and Cloud Computing. I'm applying that knowledge on top of a strong industry track record: systems serving 200+ enterprise clients, 99.9% uptime SLAs, and measurable wins like cutting batch failures by 90% and boosting sales by 15% through data-driven features.
My technical stack spans Java, Spring Boot, Python, AWS, Kafka, Kubernetes, and React — and I'm actively building in the AI space: LLMs, RAG pipelines, prompt engineering, and cloud-based ML services.
I'm looking for roles at the intersection of software engineering and AI — building AI-powered products, integrating LLMs into backend systems, or working on ML infrastructure. Open to on-site, hybrid, or remote opportunities in the US.
I bring a rare combination of 7+ years of production engineering expertise and hands-on AI/ML skills built through my Master's program. I don't just understand AI in theory — I've built RAG pipelines, multi-step AI agents, and ML-ready data infrastructure that powers real systems.
My goal is to become an engineer who builds AI solutions that are robust, explainable, and production-ready. I understand how LLMs, guardrails, and ML pipelines must integrate with real backend infrastructure — because I've spent years building that infrastructure.
This portfolio is designed for technical hiring managers, engineering leaders, and academic evaluators in the software engineering and AI/ML space. It is relevant to anyone looking for a candidate who combines hands-on backend and cloud engineering experience with a principled understanding of machine learning concepts, ethical AI practices, and change leadership.
Whether you are evaluating me for a software engineering role, an AI/ML engineering position, or a graduate academic program, this portfolio demonstrates both the technical depth and the reflective, ethics-driven mindset that modern AI/ML teams need.
Also experienced with: RAG Pipelines, OpenAI API, TF-IDF, Kafka, RabbitMQ, Databricks, MySQL, PostgreSQL, MongoDB, Oracle/PL-SQL, Snowflake, Docker, Kubernetes, Jenkins, GitHub Actions, JUnit, Mockito, Testcontainers, Linux, CI/CD.
🤖 Game Glitch Investigator v2 — Applied AI System
Python · OpenAI GPT-4o · RAG · Streamlit · pytest
Built a full applied AI system (AI110, IWU) that auto-diagnoses bug reports using a 6-step multi-agent reasoning pipeline: input guardrails, TF-IDF RAG retrieval, GPT-4o diagnosis, self-critique loop, and confidence-scored investigation reports. Achieved 10/10 evaluation harness pass rate and 100% RAG retrieval accuracy. 50 unit tests.
ML-Ready ETL & Data Pipeline
Spring Batch · Kafka · AWS S3/Lambda
Built AI-ready ETL pipelines and AWS data lake infrastructure at Paltel to process millions of telecom records daily — producing clean, structured datasets ready for ML model training and intelligent system integrations.
Sales-Channel Redesign
Spring Boot · React · Kafka · MySQL
Migrated legacy JSP sales channel to Spring Boot + React, improving performance by 80%. Built event-driven cart abandonment feature leveraging real-time behavioral signals — a core pattern in recommendation and prediction systems.
Workshop 1 — AI Lab
AIML-500 · Machine Learning Fundamentals
Hands-on exploration of ChatGPT prompt engineering, Consensus Custom GPT for academic research, STORM AI article generation, and a Chatbase chatbot prototype built with Design Thinking.
Skills: Prompt Engineering, Custom GPTs, AI Research Tools, Design Thinking, Chatbot Prototyping, Critical AI Evaluation
View ArtifactWorkshop 2 — ML vs. Deep Learning
AIML-500 · Machine Learning Fundamentals
Collaborative group presentation comparing Machine Learning and Deep Learning — covering pipelines, neural networks, real-world applications, and a decision framework for choosing the right approach.
Skills: Technical ML/DL Knowledge, Technical Communication, Teamwork, Presentation Design, Critical Analysis
View ArtifactWorkshop 3 — ML Training Methods
AIML-500 · Machine Learning Fundamentals
Interactive AI coaching session exploring supervised, unsupervised, and reinforcement learning, algorithm selection, training pipelines, iteration, and the critical role of data quality.
Skills: ML Training Concepts, Interactive AI Learning, Critical Thinking, Technical Problem Solving, Self-Directed Inquiry
View ArtifactWorkshop 4 — Data Challenge Scenarios
AIML-500 · Machine Learning Fundamentals
Scenario-based simulation working through three real-world ML data challenges: missing data handling, class imbalance and fairness evaluation, and data privacy with scalability constraints.
Skills: Data Cleaning & Imputation, ML Fairness & Bias Auditing, Differential Privacy, Trade-off Analysis, Responsible ML
View ArtifactWorkshop 6 — Personal Framework: Change Leader
AIML-500 · Machine Learning Fundamentals
A personal AI/ML leadership framework developed at course completion — including a mission statement, six core values, measurable objectives, short- and long-term action plans, and a structured evaluation and adaptation process.
Skills: AI/ML Change Leadership, Ethical AI Governance, Strategic Planning, Responsible Innovation, Self-Assessment, Stakeholder Communication
View Artifact2019 – Present
Paltel (Palestine Telecommunications) — one of the largest telecom companies in the Middle East, providing connectivity and digital services to 200+ enterprise clients across the region.
- Built AI-ready ETL pipelines (Spring Batch) to ingest and transform daily CRM data feeds, reducing batch failures by 90% — producing clean, structured datasets ready for ML model training.
- Engineered event-driven cart abandonment feature (Spring Boot, Kafka, MySQL), boosting sales by 15% — leveraging real-time behavioral signals, a core pattern in recommendation and prediction systems.
- Designed ML-ready AWS data lake (Lambda, S3, SNS) for automated file archival, cutting storage costs by 8% — scalable cold storage architecture for AI/ML training datasets.
- Maintained 24×7 data platform (200+ enterprise clients, 99.9% SLA) with automated health-check alerting — observability patterns directly applicable to ML infrastructure monitoring.
- Optimized MongoDB and MySQL schemas (RDS, S3), improving query performance by 40% — directly applicable to feature store design and efficient ML data retrieval pipelines.
- Streamlined CI/CD pipelines (GitHub Actions, Jenkins, Docker, AWS) with blue-green deployments on EC2 — aligned with MLOps practices for continuous model delivery.
- Built RESTful APIs (Flask, Spring Boot) for large-scale data-heavy applications — core infrastructure pattern for AI model serving and intelligent microservice backends.
- Led full-stack redesign of Sales Channel app (JSP → Spring Boot + React), improving performance by 80% and enabling data-driven UX for future ML feature integration.
- Achieved 100% unit and integration test coverage (JUnit, Mockito, TestContainers) — TDD discipline essential for AI/ML pipeline reliability.
Jan 2026 – Aug 2027
Master of Computer Science
Indiana Wesleyan University
GPA: 4.0 | Key Courses: Machine Learning, Artificial Intelligence, Deep Learning, Natural Language Processing (NLP), Big Data Analytics, Cloud Computing, Neural Networks, Applied Software Development.
2015 – 2019
Bachelor of Computer Science
Birzeit University
Studied algorithms, DBs, OS, networks, AI, and full software engineering curriculum.
Cloud & AI
- AWS Certified Solutions Architect – Associate
- AWS Certified Cloud Practitioner
- ChatGPT Prompt Engineering
Software Engineer
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Professor
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Address
Sacramento, CA, USA
Phone
+1 641-233-9526
mohammedzjasaad@gmail.com