Iman Irajian

I specialize in transforming AI research into production-ready systems that are scalable, reliable, and maintainable in enterprise environments.

AI Engineer specializing in production Machine Learning systems, LLM applications, and scalable distributed software.

I design and build intelligent systems that bridge artificial intelligence and production-grade software engineering. Over the past 10+ years, I've developed large-scale platforms for the fintech industry, combining modern AI with reliable backend architecture to solve real business problems at scale.

My expertise spans the complete AI lifecycle, from data engineering and model development to MLOps, cloud deployment, and highly available distributed systems. I enjoy turning research ideas into production-ready products that are scalable, observable, secure, and maintainable.

Recent areas of focus include:

• Machine Learning for fraud detection, anomaly detection, credit scoring, and predictive analytics
• Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, and enterprise Generative AI solutions using OpenAI, LangChain, and Spring AI
• End-to-end MLOps platforms with MLflow, Docker, Kubernetes, GitLab CI/CD, and automated model deployment
• High-performance backend systems using Java 21, Spring Boot, Spring Cloud, Redis, Kafka, and cloud-native microservices serving more than 80 million banking users

Alongside my AI work, I have extensive experience designing distributed systems with a strong emphasis on scalability, resilience, observability, and security. This software engineering foundation enables me to build AI systems that are not only accurate, but also reliable in production.

I hold an M.Sc. in Computer Engineering from the University of Tehran, graduating as a First Rank Honor Student. My research has been published by IEEE, focusing on deep learning and recommender systems.

I enjoy solving challenging engineering problems, mentoring engineers, and working at the intersection of AI research and large-scale software engineering.

Core expertise:

Machine Learning • Deep Learning • LLMs • Generative AI • AI Agents • NLP • RAG • MLOps • Python • TensorFlow • PyTorch • Scikit-learn • Java • Spring Boot • Distributed Systems • Kubernetes • Docker • Cloud-Native Architecture