Summary
ZAEEM AHMED is a DevOps Engineer with hands-on experience building CI/CD pipelines, managing containerized applications, and deploying production workloads. He is skilled in Docker, Kubernetes, and cloud infrastructure, with a strong foundation in distributed systems and ML deployment. He is currently pursuing a Bachelor's in Data Science with a focus on MLOps and scalable data platforms.
Education
| Bachelor of Science in Data Science | Expected Graduation: 2026 | FAST National University of Computer and Emerging Sciences (NUCES), Islamabad |
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Qualifications and Training
- Internship Certificate – Parallel and Computing Lab, FAST NUCES
- Certificate of Recognition – Hackathon Organizer (Upstreet SDK, AI Agents)
Work Experience
– Present
Moqah.pk
DevOps Engineer
- Designed and implemented CI/CD pipelines using GitHub Actions, automating deployment workflows and reducing manual intervention
- Managed complete domain infrastructure including domain acquisition, DNS configuration, and subdomain setup for production services
- Deployed and maintained production workloads on Hetzner cloud servers, managing server provisioning and resource optimization
- Configured Nginx web servers with reverse proxy setups, managed SSL/TLS certificates, and implemented traffic routing strategies
- Deployed and managed testing branches to isolated test servers, ensuring safe pre-production validation
- Implemented monitoring and observability solutions using Prometheus and Grafana for system health tracking and alerting
- Troubleshot production deployment issues, performed root cause analysis, and optimized infrastructure for improved reliability
⚠ Review: start date missing or unclear
Jun 2024 – Aug 2024
Parallel and Computing Lab, FAST NUCES
DevOps Intern
- Gained hands-on experience with distributed machine learning systems and DevOps workflows
- Containerized ML applications using Docker and orchestrated multi-container deployments with Kubernetes
- Contributed to distributed computing projects using Ray framework for parallel ML workloads
- Collaborated on optimizing infrastructure for ML training and inference workloads
Projects
- AI Model Engineering & MLOps: Trained and deployed multiple deep learning architectures including CNNs, LSTMs, RNNs, and Transformers; implemented model quantization techniques to reduce model size for deployment on resource-constrained devices (Raspberry Pi); optimized models for low-latency inference on edge devices, balancing performance and resource utilization.
- Personal Home Lab Infrastructure: Designed and maintain self-hosted infrastructure as alternative to cloud storage services; implemented automated backup system with global accessibility and secure remote access; deployed Prometheus and Grafana stack for comprehensive system monitoring, metrics collection, and visualization; configured alerting rules for proactive incident detection and service health monitoring; managed network security, containerized services, and data redundancy across distributed storage; set up custom dashboards for tracking system performance, resource utilization, and service availability.
- Music Recommender System: Developed production-ready ML recommendation engine using Python and MongoDB; implemented data pipeline for processing audio features and training recommendation models; deployed scalable backend API for real-time song recommendations.
- Data Warehouse & Streaming Pipeline: Designed star-schema data warehouse in MySQL for analytical workloads; simulated real-time data ingestion using multi-threaded Java application; built web scraper with Python and Beautiful Soup, integrated with Apache Kafka for distributed data streaming; implemented topic-based data organization for parallel processing and analysis.
Languages
- Urdu (Native)
- English (Full Professional Proficiency)
Skills