Professional background
AI and Machine Learning engineer focused on building production-ready ML systems, NLP tooling, retrieval-augmented generation workflows, analytics pipelines, and applied data products. My work combines machine learning, backend structure, and product-oriented design to turn models into usable systems rather than isolated experiments.
How I approach AI engineering
My work sits at the intersection of machine learning engineering, applied analytics, NLP system design, and AI product architecture. I am especially interested in systems where the challenge is not only training a model, but building the full workflow around it.
Capabilities beyond the website projects
The projects on this site are only one part of my profile. Beyond them, I have experience with analytical workflows, financial datasets, predictive modeling, ETL-style data processing, structured experimentation, and translating business questions into measurable ML or analytics tasks.
Experience at Ayandehsaz Pension Fund
In addition to portfolio projects, I have worked on real analytical and predictive tasks in the pension and financial domain. That experience strengthened my ability to work with business-sensitive data, quantitative workflows, and production-minded analytics.
Ayandehsaz Pension Fund
Worked on financial and pension-related analytical tasks involving large-scale datasets, predictive analysis, trend interpretation, and support for portfolio monitoring and risk-related evaluation.
Type of work delivered
Built Python-based analytical workflows, data processing pipelines, and predictive analysis logic for working with historical financial indicators, pension-related information, and decision-support style reporting.
What that experience added
It expanded my understanding of how ML and analytics fit into real organizations, especially in cases where interpretability, data quality, structured processing, and business reliability matter as much as raw model performance.
Where I spend most of my attention
My strongest interest is in AI systems that combine data, modeling, backend logic, and usable interfaces into a coherent product-like experience.
End-to-end ML systems
Building full ML workflows from dataset understanding and feature work to evaluation, serving, and monitoring.
NLP and LLM-powered assistants
Designing question answering, summarization, text processing, and assistant experiences with grounding and traceability.
Retrieval-Augmented Generation
Creating document-aware AI systems where chunking, embeddings, retrieval quality, and evidence visibility are central.
MLOps, observability, and reliability
Structuring artifacts, dashboards, registries, and engineering patterns that make ML systems easier to trust and maintain.
What the full profile says
Across both portfolio projects and professional work, the common thread is systems thinking: using data and machine learning to build structured, useful, and explainable tools rather than isolated demos.
What I bring technically
I am most effective in environments where modeling depth and engineering structure both matter.
Where I want to keep growing
I want to continue moving deeper into ML engineering and AI systems roles where I can work on production-quality tools, reliable pipelines, and applied AI products with real business impact.