Available for select collaborations

Engineering Production-Ready
LLM & Agentic
AI Systems.

I'm Alireza Aliabadi — an AI Software Engineer designing production-ready GenAI, LLM, Agentic AI, and RAG applications. Combining a deep DevOps and cloud-native background with modern AI technologies to build scalable, resilient, and observable intelligent systems.

4+
Years building
5+
AI Projects Shipped
10+
Certifications
∞
Curiosity
Alireza Aliabadi GitHub Profile Picture
Engineer · Architect
Alireza Aliabadi
Remote
01 — Profile

A note on practice

The work lives at the intersection of rigorous cloud engineering and applied artificial intelligence — where infrastructure meets cognition.

Alireza Aliabadi is an AI Software Engineer with a strong foundation in DevOps and cloud-native architecture. He specializes in designing and developing production-ready Generative AI, LLM, Agentic AI, RAG, and MCP applications.

Experienced in building highly scalable backend systems using Python, Go, and TypeScript, coupled with extensive expertise in Docker, Kubernetes, and cloud infrastructure. His recent work involves NLP agentic platforms, AI API integrations, multimodal document processing, and healthcare-focused RAG applications.

He brings a unique perspective to AI development: ensuring that intelligent systems are not just functionally accurate, but robust, monitored, and seamlessly deployed in enterprise environments.

"Intelligence is nothing without infrastructure. Scale AI like you scale code."
Currently
Roles AI Eng @ AiSmoothy
Focus LLMs · Agents · RAG
Languages Python · Go · TS
DevOps K8s · Docker · AWS
Contact +98 910 807 7006
02 — Capabilities

Areas of depth

A blend of artificial intelligence engineering and deep DevOps expertise, ensuring models move from notebook to production seamlessly.

Applied AI & LLMs

Designing Agentic AI workflows, Retrieval-Augmented Generation (RAG), and prompt engineering. Integrating LLM APIs for robust content generation and Q&A systems.

Agentic AI RAG Prompt Engineering Embeddings Semantic Search NLP

Backend Development

Building cloud-native, high-performance microservices and API gateways. Refactoring monolithic apps into scalable, distributed systems.

Golang Python Rust FastAPI TypeScript React PostgreSQL Redis

DevOps & Cloud Native

Orchestrating containerized applications with zero downtime. Implementing CI/CD pipelines, infrastructure as code, and comprehensive monitoring.

Docker Kubernetes AWS Azure Terraform Ansible Helm

Monitoring & Data

Designing real-time data pipelines, observability stacks, and automated alerting. Managing vector databases and object storage for AI workloads.

Prometheus Grafana Elasticsearch MongoDB MinIO/AIStor RabbitMQ Consul
03 — Trajectory

Selected work

An evolution from optimizing infrastructure and DevOps pipelines to architecting advanced AI and LLM solutions.

AI Software Engineer @ AiSmoothy

Remote · Jul '26 – Present
Jul '26 — Now

Leading the development of NLP agentic solution platforms, including Q&A assistants and intelligent analyzers, bridging AI research with robust production infrastructure.

  • Developed comprehensive NLP agentic solution platforms for automated analysis and assistance.
  • Implemented highly available AI API integrations using a Go-based gateway service with advanced caching mechanisms.
PythonGolangAgentic AI LLMsAPI GatewayCaching

DevOps Engineer @ Sharif Technology Services Complex

Feb '25 – Present
Feb '25 — Now

Managing and optimizing cloud infrastructure and deployment pipelines for enterprise-grade applications.

KubernetesDockerCI/CD LinuxTerraform

DevOps Engineer @ Azki Sarmayeh

Jun '24 – Oct '24
Jun '24 — Oct '24

Optimized deployment architectures and monitoring systems for .NET and React applications, significantly improving resource tracking and service discovery.

  • Optimized .NET and React dockerized deployments using Docker multistage builds.
  • Implemented container usage monitoring and alerting using cAdvisor, Prometheus, and Grafana.
  • Implemented RabbitMQ service discovery using Consul for resilient microservices communication.
DockerPrometheusGrafana RabbitMQConsulcAdvisor

DevOps Engineer @ Hamrahe Aval (MCI)

Feb '22 – Dec '23
Feb '22 — Dec '23

Drove major infrastructure improvements, reducing build times, optimizing monitoring, and migrating monolithic architectures to scalable microservices.

  • Refactored monolithic backend API services to microservices architecture using Python, FastAPI, and Docker.
  • Reduced development time by 60% by developing a USSD code menu creator and data models handler in Python.
  • Reduced monitoring resource and time cost from 8 minutes to 5 seconds by developing Python/Golang exporters for Zabbix/Prometheus.
  • Achieved zero downtime and scalability by implementing Kubernetes orchestration; minimized Android APK build time from 20 mins to 1 min via Gradle CI.
  • Deployed Elasticsearch cluster and Kibana for log monitoring; automated deployments with Ansible.
KubernetesFastAPIGolang AnsibleELK StackJenkins
04 — Portfolio

Selected projects

A selection of AI platforms, medical imaging pipelines, and real-time data systems built with production-grade architecture.

Agentic AI · LLM

ContentForge AI Agent

An AI-powered multi-agent content creation platform leveraging LLM APIs, RAG, multimodal document processing, and automated quality evaluation to generate high-fidelity content.

Multi-Agent RAG
Healthcare · RAG

AI Medical Knowledge Assistant

A healthcare-focused RAG application combining vector search, semantic retrieval, prompt engineering, and LLM APIs to deliver grounded, accurate medical question answering.

Healthcare Semantic Search
Medical Imaging

DICOM PHI MONAI Pipeline

Built a privacy-preserving medical imaging pipeline for DICOM PHI de-identification and MONAI-compatible preprocessing to support scalable, compliant healthcare AI workflows.

PHI De-id MONAI
Data Pipeline · ML

Real-Time AI Anomaly Detection

A real-time data processing pipeline that detects anomalies in sensor data using machine learning. Built with FastAPI, Redis Streams, and scikit-learn's IsolationForest algorithm.

Real-time Redis Streams
Document Intelligence · Full Stack

AI-Powered Document Intelligence Platform

A production-style AI document intelligence platform built with Django, React, TypeScript, Celery, Redis, MongoDB, and AIStor (S3-compatible object storage). Features a full ingestion pipeline, text extraction, chunking, embedding generation, semantic search, and RAG-based natural language Q&A with async processing.

React Django AIStor Q&A
05 — Credentials

Education & certifications

A foundation in Information Technology backed by industry-recognized certifications in AI, Cloud, and DevOps.

Bachelor's degree in Information Technology

Islamic Azad University Central Tehran Branch · '16 – '21
High School Diploma in Mathematics from MandegarAlborz (Math Olympiad team).

Associate AI Engineer for Developers

DataCamp · Jun '26

Secure AI/ML Driven Software Development

Linux Foundation · Feb '26

AWS Certified AI Practitioner

KodeKloud · Feb '26

Azure AI Engineer Associate

KodeKloud · Feb '26

Certified Kubernetes Application Developer (CKAD)

Cloud Academy · Oct '23

Azure DevOps Solutions

Cloud Academy · Aug '23
06 — Get in touch

Let's build something
intelligent.

Open to AI engineering roles, DevOps + AI hybrid positions, and advisory engagements. Let's discuss how we can scale your next intelligent system.

alireza.aliabadi.dev@gmail.com