Farhad Davaripour, PhD.

Tech Lead, Software Engineer, and Machine Learning Engineer with ~10 years of multidisciplinary experience across engineering, software development, analytics, data science, and GenAI. I design and ship production software, ML, and LLM applications (RAG, agentic workflows/ReAct, function calling), and build scalable data/ML pipelines on Databricks/Spark across Azure and AWS. What sets me apart is my strength in system design, distributed performance optimization, and rigorous validation to deliver reliable solutions under real enterprise constraints (security, latency, and cost).

Let's meet

Webinar Series

Arcurve Webinar Series Ep. 1: Research Agent - An Application of Agentic Workflow

Arcurve Webinar Series Ep.2: Deep Dive into the Research Agent Leveraging AI Agentic Workflows

Arcurve Webinar Series Ep.3: Bridging AI and Engineering Standards - Design & Compliance Handling

Arcurve Webinar Series Ep.4: Augmenting Simulation Engineering with AI Agents: Integrating Finite Element Analysis With Agentic AI

Arcurve Webinar Series Ep.5: Multi-Modal RAG for Interprises, Seamless SharePoint Integration (1/2)

Arcurve Webinar Series Ep.6: Multi-Modal RAG for Interprises, Seamless SharePoint Integration (2/2)

Educational Contents/Apps

1. Linear Regression Core Principles

In this lecture you will learn how to build a linear regression model from scratch.

GitHub page: Link

Run demo notebook on Google Colab: Link

2. Next Token Prediction

In this lecture, you will learn how Temperature and Top-P control the randomness in LLM-generated outputs

Dashboard: Link

3. Intro to Prompt Engineering

In this lecture, you will learn how to implement the most popular prompt engineering techniques.

GitHub page: Link

Run demo notebook on Google Colab: Link

4. Intro to Retrieval Augmented Generation (RAG)

In this lecture, you will learn how to implement a basic RAG pipeline.

GitHub page: Link

Run demo notebook on Google Colab: Link

5. Intro to LLM-based Function Calling

In this lecture, you will learn how to implement a basic function calling workflow.

GitHub page: Link

Run demo notebook on Google Colab: Link

6. ReAct Agentic Implementation

This lectures goes through an implementation of a ReAct Agentic Workflow from scratch.

GitHub page: Link

App: Link

7. AI in Pipeline Engineering

This lectures goes through an end to end ML workflow to train a pipeline ILI data analyzer.

GitHub page: Link

Colab Notebook: Link

App: Link

8. GenAI in Pipeline Engineering

This lectures goes through a development of a ReAct Agentic Workflow to parse and execute ALA 2005 design guideline for buried steel pipeliens.

GitHub page: Link

App: Link

9. AI Assisted FEA

This lecture explores implementing Abaqus (a Finite Element Analysis tool) as part of a ReAct agent powered by OpenAI's LLM, automating finite element simulations, including input generation, job execution, stress analysis, parametric studies, and sensitivity analysis, streamlining workflows for improved decision-making.

GitHub page: Link

Distilled Notes

Stanford-CS229-Spring2023

CS229 course notes from Stanford University on machine learning, covering fundamental concepts and algorithms.

GitHub page: Link

App: Link

Educational Contents for Pipeline Engineers

AI in Pipeline Engineering

This course explores various machine learning (ML) implementations specifically targeting preventative maintenance in pipeline engineering challenges.

GitHub page: Link