As an MLOps developer, simulation specialist, and research engineer with over 6 years of experience, I specialize in developing innovative data-driven solutions. My tenure as a postdoc at NC Inc. was marked by leveraging machine learning and synthetic data from finite element simulations for thermal stress analysis in pipe bends, directly addressing industry challenges. In my current role at Arcurve Inc., I utilize cloud services (e.g., Azure and AWS) to develop/maintain end-to-end machine-learning pipelines. Additionally, my personal project 'DocsGPT,' a RAG-based querying app using LangChain and OpenAI's API, demonstrates my proficiency in implementing solutions using generative AI. Let's connect on LinkedIn.
🙂
Farhad is a Machine Learning developer with a knack for problem-solving and a passion for data science.
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Arcurve Inc.
- Calgary
- https://www.linkedin.com/in/farhad-davaripour/
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ReAct_Agent_from_Scratch
ReAct_Agent_from_Scratch PublicThis repository provides an introductory implementation of the ReAct (Reasoning and Acting) Agentic workflow.
Jupyter Notebook
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Stanford-CS229-Spring2023-Notes
Stanford-CS229-Spring2023-Notes PublicCS229 course notes from Stanford University on machine learning, covering lectures, and fundamental concepts and algorithms. A comprehensive resource for students and anyone interested in machine l…
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AI_Applications_in_Pipeline_Engineering
AI_Applications_in_Pipeline_Engineering PublicJupyter Notebook
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CFRP_Reinforced_HDD_overbend
CFRP_Reinforced_HDD_overbend PublicThis project employs machine learning and synthetic dataset to predict the peak equivalent stress imposed on a CFRP wrapped HDD overbend
Python
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