Shreyas Kori
@shreyaskori
Machine learning expert applying AI-driven predictive analytics to improve industrial reliability and efficiency.
What I'm looking for
I am a machine learning expert who applies deep learning, probabilistic modeling, and Python-driven automation to solve industrial engineering challenges. My work focuses on time-series forecasting, predictive maintenance, and risk-based inspection for real-world assets.
At BP (Aberdeen) I developed probabilistic degradation and corrosion models, integrated condition-monitoring data into predictive pipelines, and helped reduce unplanned downtime by 30%. I have built deep learning architectures (BiLSTM, CNN-LSTM) for RUL forecasting of turbofan engines and applied reinforcement learning to optimize maintenance scheduling.
In industry roles I created Python automation for design validation, built preprocessing and visualization pipelines with SQL and Tableau, and integrated sensor/IoT streams into condition-monitoring frameworks. I consistently delivered improvements in efficiency, documentation accuracy, and project delivery timelines.
I combine a mechanical systems background with advanced AI/ML skills and practical tools (TensorFlow, PyTorch, SQL, Tableau) to bridge assets with intelligent automation, driving better decision-making, reliability, and operational efficiency.
Experience
Work history, roles, and key accomplishments
Machine Learning Engineer
British Petroleum
Apr 2024 - Dec 2024 (8 months)
Designed deep learning pipelines (BiLSTM, CNN-LSTM) and RL-based scheduling for RUL forecasting and predictive maintenance, integrating condition-monitoring with FEA to reduce unplanned downtime by 30%. Evaluated models using RMSE/MAE and improved AI diagnostic interpretability for reliability benchmarking.
Project Engineer
Gunnam Infra Pvt Ltd.
Jul 2020 - Dec 2023 (3 years 5 months)
Developed Python automation and data pipelines, reducing documentation errors 25% and saving 200+ engineering hours annually; implemented predictive models and ML-driven process optimizations that accelerated project delivery by 20%.
Research Project Intern
A*STAR Institute of High Performance Computing
Oct 2019 - Jun 2020 (8 months)
Built deep learning (BiLSTM, CNN-LSTM) RUL forecasting models for turbofan engines, applied RL for maintenance scheduling, and validated time-series pipelines using Python, improving model evaluation and reliability metrics.
Education
Degrees, certifications, and relevant coursework
University of Aberdeen
Master of Science, Advanced Mechanical Engineering
2024 - 2025
Master of Science in Advanced Mechanical Engineering with coursework in FEM, optimization, CFD, and a dissertation on AI-driven probabilistic modeling of topside structure corrosion for risk management.
Coventry University
Bachelor of Engineering (Hons.), Mechanical Engineering
2017 - 2020
Bachelor of Engineering (Hons) in Mechanical Engineering with a dissertation on deep learning for turbofan engine remaining useful life prediction using CNN-LSTM and Python.
Tech stack
Software and tools used professionally
Availability
Location
Authorized to work in
Job categories
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