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🔭 AI/ML Engineer with hands-on experience in Deep Learning, Computer Vision, NLP, and Generative AI. Skilled in building and deploying machine learning solutions using MLOps practices and FastAPI. Strong problem-solving abilities, continuously enhanced through DSA in Python and real-world AI projects**
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🌱 I’m currently learning Gen-AI
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👯 - 👯 Looking to collaborate on AI/ML projects, research-work, and Hack-A-Thons.
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💬 - 💬 Ask me about AI/ML, Deep Learning, NLP, Computer Vision, Gen-AI, and MLOps.
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📫 How to reach me OsamaShabih@st.jamiahamdard.ac.in
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📄 Know about my experiences https://drive.google.com/file/d/1d46vvNGxWNr2mmUQhBpRVHfVpNQuHjeQ/view?usp=drive_link
| Category | Tools & Technologies |
|---|---|
| 🚀 Neural Frameworks |     |
| 👁️ Computer Vision | YOLO (v8/v10), CNN Architectures, Image Segmentation, Object Tracking, OpenCV, Face Recognition |
| 🔤 Natural Language |  LLMs, Fine-tuning, spaCy, NLTK, BERT, GPT-based Models |
| ⚡ Production & Scale |     |
| 💻 Core Languages |     |
- Neural Design: Custom CNN layers development, Weight initialization strategies, and Hyperparameter tuning.
- Spatial Tracking: Real-time 3D landmark detection and coordinate mapping (Hand/Face/Body).
- Optimization: Model Quantization and Pruning for edge deployment.
- Data Engineering: Designing automated data pipelines using Selenium and BeautifulSoup for training custom models.
I bridge the gap between Data Science and Software Engineering by implementing robust MLOps practices. My focus is on creating reproducible, scalable, and automated pipelines that transform experimental models into reliable production services.
- Workflow Engines: Orchestrating complex ML DAGs using Apache Airflow and Kubeflow for seamless data-to-model transitions.
- Metadata & Lineage: Using ZenML to create framework-agnostic pipelines and track data/model lineage.
- Data Versioning: Implementing DVC with YAML configurations to ensure 100% reproducibility of datasets and experiments.
- Docker: Containerizing ML environments to eliminate "it works on my machine" issues.
- Kubernetes (K8s): Managing distributed clusters for high-availability model serving and resource scaling.
- Serverless AI: Deploying lightweight models using AWS Lambda for cost-efficient, event-driven inference.
- GitOps Mastery: Using ArgoCD for declarative continuous deployment on Kubernetes.
- Automation: Building GitHub Actions pipelines for automated linting, testing, and container pushing.
- Version Control: Expert use of Git & GitHub for collaborative development and code branching strategies.
- Cloud Infrastructure: Architecting scalable environments on AWS (EC2, S3, EKS).
- Observability: Real-time system health tracking using Prometheus for metrics and Grafana for advanced visual dashboards.
- REST APIs: Developing high-performance, asynchronous interfaces with FastAPI.
| Category | Tools & Technologies |
|---|---|
| Orchestration |    |
| Infrastructure |    |
| Deployment |   Serverless |
| Monitoring |   |
| Versioning | DVC, YAML, Git/GitHub, MLflow |
I implement and manage industry-standard deployment patterns to ensure zero downtime and model reliability:
- Blue-Green Deployment (Instant switch)
- Canary Deployment (Incremental traffic)
- A/B Testing (Statistical comparison)
- Shadow Deployment (Real-world testing without impact)
- Recreate Strategy | Rolling Updates | Multi-Region Deployment | Serverless Scaling
GenAI & Specialized Tools
aj01
Last Edited on: 10/04/2025