Mahdi Shooshtari ^{Top-Executive}

I’m a Machine Learning Engineer focused on building scalable ML pipelines and production-ready AI systems. I specialize in MLOps, cloud deployment (Azure, GCP), and turning complex data into real-world impact through collaboration and innovation. I’ve designed and implemented analytical applications that enhance efficiency and decision-making across industrial environments, and I’m passionate about bridging data, engineering, and business to deliver measurable results.

🎯 Core Strengths:
✅ End-to-End ML Solutions: Designing and implementing analytical applications from data ingestion to production deployment.
✅ MLOps & Cloud Deployment: Building scalable pipelines and automating workflows using Databricks, MLflow, Docker, and Azure DevOps.
✅ Predictive Modeling: Applying advanced Machine Learning and Deep Neural Network algorithms to improve industrial performance.
✅ Cross-Functional Collaboration: Partnering with data engineers, application developers, domain experts, and decision-makers to translate business challenges into actionable AI solutions.
✅ Applied AI Systems: Developing LLM-powered and computer vision–based systems that deliver measurable business impact.

⚙️ Technical Skills
✔️ LLM & GenAI: LangChain, LangGraph, RAG, Model Evaluation, OpenAI API, Hugging Face
✔️ ML & AI Frameworks: scikit-learn, TensorFlow, PyTorch, FastAI, MLflow
✔️ Computer Vision & Deep Learning: OpenCV, Mask R-CNN, InceptionV3, UNet, ResNet34
✔️ MLOps & Cloud Tools: Databricks, Azure (Certified), GCP, Docker, Azure DevOps
✔️ Data Engineering: MySQL, PostgreSQL, data pipelines, ETL workflows
✔️ Languages: Python, SQL, Java, MATLAB
✔️ Other Tools: CI/CD, Azure Container Registry, Agile/Scrum