AI Engineering · Backend Systems
AI systems from model to product.
Turning AI ideas into usable systems: systems that do not only run models, but understand data, use tools, make decisions, and fit into real product workflows.
02 / WORK
Selected projects
03 / CAPABILITY
Engineering focus
The areas I keep practicing across AI projects, backend systems, and product-shaped prototypes.
Grounded AI systems
RAG pipelines, citation-aware answers, evaluation checks, and clear behavior when reliable evidence is missing.
Controlled model behavior
Placing LLM decisions behind routing, validation, permissions, and product rules instead of trusting raw model output.
Backend product systems
Domain models, APIs, migrations, queues, auth, billing flows, and lifecycle states for real product workflows.
Agent workflows
Stateful orchestration, tool use, human approval points, memory, and recovery from partial failures.
04 / BACKGROUND
About
My path began in psychology. I was looking for a new way to create meaningful work in that field when a machine learning course introduced me to a different direction. The possibilities of AI convinced me to focus on learning one of today's most practical and fast-moving technologies.
Along the way, I have studied Python, statistics, data analysis, machine learning and deep learning, text processing, APIs, databases, Docker, system design, and MLOps. I am especially interested in agentic AI, RAG systems, and practical products built around language models.
I currently turn what I learn into end-to-end practice projects and working prototypes. I approach problems through benchmarks, data-driven tests, and evaluation so that building becomes a way to deepen and verify my understanding, not just produce an output.
I am at the beginning of my professional AI engineering journey and looking for my first opportunity to contribute in a real team, take responsibility, and grow alongside experienced engineers. My psychology background also helps me keep human behavior and real user needs in view while building technology.
Building as practice
Most of my learning happens through building: RAG systems, agent workflows, backend services, and product-shaped prototypes that force ideas to meet real constraints.
Current focus
I am currently deepening my knowledge of agentic AI and RAG by building personal prototypes, testing ideas with measurable criteria, and documenting what I learn.
Build process
I usually start small, make assumptions explicit, measure behavior, study failures, and then harden the system step by step.
Education
B.A. in Psychology, Imam Reza University
05 / TOOLKIT
Technical skills
AI Engineering
Backend
Data
Product & Web
Infrastructure
06 / SOFT SKILLS
Soft skills
The habits I try to bring into teamwork, learning, and real project environments.
Clear communication
I explain decisions, constraints, and blockers early so collaboration stays calm and practical.
Feedback-ready teamwork
I am comfortable asking questions, receiving critique, and turning feedback into concrete next steps.
Fast, disciplined learning
I learn by building small versions, documenting what changes, and testing assumptions against real behavior.
Structured problem solving
I break vague problems into observable parts, compare options, and keep the reasoning traceable.
Ownership
When I take on a task, I care about the outcome, the handoff, and the parts that need follow-through.
User empathy
My psychology background helps me notice where product behavior meets real human needs and confusion.
07 / CONTACT
Let’s talk about AI projects and opportunities to build.
For relevant engineering conversations, reach out by email, LinkedIn, or GitHub.