Shah Abedinn
I'm Shah Abedinn, an AI Engineer and Machine Learning Developer with 3+ years of experience building and deploying production-ready AI systems and automation workflows that integrate directly into business operations.
About
I’m Shah Abedinn, an AI Engineer and Machine Learning Developer with 3+ years of experience building and deploying production-ready AI systems and automation workflows that integrate directly into business operations. I design AI-driven agents that ingest data from multiple sources, apply scoring logic and ML models, and trigger automated actions through APIs. I translate unstructured and semi-structured data into actionable signals using Python, NLP, and Generative AI (LLMs), and I build end-to-end ML pipelines covering data ingestion, feature engineering, model evaluation, deployment, and monitoring.
I have a track record of delivering measurable business impact through automation and intelligent decision systems, and I enjoy collaborating with non-technical stakeholders to ensure AI solutions align with real-world requirements and deliver tangible outcomes.
- 8 years' experience
- Joined January 2026
Languages
- EnglishFluent
Industry experience
- Energy & Utilities
- Software & Internet
- Manufacturing
- Education
- Government
- Professional Services
More experts in Vancouver, Canada
Work experience· 4 roles
Built ML models in Python and TensorFlow for early anomaly detection, improving maintenance efficiency by 20%. Automated structuring of unstructured log sheets using Python, Scala, LLMs (HuggingFace) and Raspberry Pi, reducing manual review time by 40%. Developed a TensorFlow convolutional model for real-time cloud monitoring, boosting inference speed by 18%.
Built and validated ML models in MATLAB and Python (Pandas, Scikit-learn) using sensor and operational data to predict condenser performance, improving system efficiency by 8% for the City of Burnaby. Also contributed to model validation and deployment tasks.
Developed and deployed predictive fault-detection models in Python using TensorFlow and scikit-learn on turbine sensor data, improving model accuracy by 15% and reducing unplanned downtime by 10%. Built real-time IoT data pipelines using FastAPI and Pandas to ingest and process streaming sensor data, enabling automated anomaly detection and low-latency inference. Developed a Docker-based experimental environment that used multi-sensor turbine data and ML models to simulate turbine behavior, leading to a 12% improvement in model accuracy.
Education· 6
Certifications and awards
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