Mobin Shahidi — AI Engineer & Software Developer

About

I work at the intersection of software systems, applied AI, and research, building scalable production systems and intelligent recommender engines with a focus on personalization, information quality, misinformation, and trustworthy political news.

⚙️ Software Systems & Production Engineering

Built and deployed a production SaaS platform end-to-end as a startup co-founder.
Designed scalable system architecture, implemented 33 REST APIs, and structured relational databases.
Delivered and maintained multiple production releases serving real users.
Developed backend systems using Python, FastAPI, ASP.NET Core, and modern web stacks.
Deployed applications using Docker and AWS with CI/CD, monitoring, and production-focused workflows.

🤖 AI/ML, Recommender Systems & Personalization

Designed personalized recommendation systems focused on relevance, trust, and user experience.
Worked on ML systems addressing misinformation and political news content.
Built large-scale data pipelines for recommendation and ranking tasks across millions of data points.
Engineered features capturing content quality, user behavior, and contextual signals.
Developed NLP and deep learning pipelines for content understanding and personalization.

🧪 Research & Applied AI Engineering

Conducted applied research in ML, NLP, explainable AI, and recommender systems.
Focused on information quality, misinformation, and trustworthy recommendation in algorithmic political newsfeeds.
Translated research ideas into production-oriented systems and scalable pipelines.
Built explainable AI solutions combining user modeling, text processing, and generation.
Emphasized reproducibility, structured experimentation, and reliable data workflows.

Skills & Expertise

🤖 AI & Machine Learning

Recommender Systems & Ranking
LLM Integration (LangChain, RAG, Fine-tuning)
Explainable AI (XAI)
Deep Learning (PyTorch, TensorFlow)
Model Evaluation & Experiment Design

⚙️ Backend Engineering

FastAPI & REST API Design
ASP.NET Core & C#
System Architecture & Modular Design
SQL Databases & Schema Design
Data Pipelines & ETL Workflows

☁️ MLOps & Infrastructure

Dockerized Model Deployment
AWS (SageMaker, CloudWatch)
CI/CD Pipelines
Model Observability & Logging
Git & Version Control

📊 Product & Research

Experimental Design & Statistical Analysis
Feature Engineering at Scale
Stakeholder Communication
Cross-functional Collaboration
Scientific Writing & Publications

Projects

Selected research and production projects in AI, recommender systems, and scalable web engineering.

Featured Projects

📰 Quality-Aware News Source Recommender

Designed and deployed a quality-aware recommender system that integrates 41 engineered information-quality signals derived from 5M+ tweets to improve news source reliability.

Recommender Systems ML Pipelines AWS Docker

Impact: Increased average recommended source quality by +3 points while maintaining baseline precision.

Stack: Python · Pandas · Scikit-learn · Docker · AWS · PyTest

📸 PhotoCans – Image Management Platform (Startup)

Co-founded and delivered a commercial full-stack web platform serving 100+ users with licensing, product management, and operational dashboards.

ASP.NET Core REST APIs SQL Server IIS

Impact: Implemented 33 REST endpoints and 12+ business workflows; deployed and maintained across multiple production releases.

Stack: C# · ASP.NET Core · JavaScript · SQL Server · IIS

📊 News Source Quality Predictor (Production ML)

Built and deployed a regression model estimating news source quality using network and diversity features, exposed via API and interactive UI.

FastAPI SageMaker Model Deployment Observability

Impact: Serverless SageMaker deployment with logging and CloudWatch monitoring for production inference.

Stack: Python · FastAPI · AWS SageMaker · CloudWatch · Docker

Research & AI Systems

🔍 Exogenous Cues to Information Quality

Engineered contextual quality variables to enhance algorithmic newsfeeds and improve information reliability in recommender systems.

Feature Engineering Evaluation Recommender Systems

Stack: Python · Scikit-learn · Statistical Modeling

💡 Deep Learning Explainable Recommender

Built a large-scale NLP + recommendation framework over 151K reviews combining multi-task learning and neural text generation to produce personalized recommendations and explanations.

PyTorch TensorFlow NLP Multi-task Learning

Impact: Achieved MSE 0.992 in rating prediction and ~0.97 semantic similarity in explanation generation.

Stack: Python · PyTorch · TensorFlow · RNN · CNN · Transformers · GPU Training

📡 Automated Reddit Scraper

Developed a scalable Reddit data collection pipeline extracting threads, comments, and interaction metadata for political discourse research.

Data Pipeline APIs NLP Data

Stack: R · Python · API Integration · Data Validation

Software & Product Engineering

🤖 Workflow-Aware AI Assistant

Built a state-aware AI assistant integrated into a Next.js / Blazor application, orchestrating OpenAI APIs and local LLMs (Mistral) using LangChain for structured task execution.

LLMs LangChain Next.js System Orchestration

Stack: FastAPI · LangChain · OpenAI · Mistral · Next.js