Your data platform
has a cost.
I make it pay off.

I'm an MLOps & Data Engineer with 9+ years designing big-data pipelines and AI infrastructure, so the systems your teams depend on run once, run right, and scale without rework.

Book a call Selected work

Recent work: founded a big-data visualization platform for the Iranian market, and shipped an LLM assistant running RAG + MCP in production.

Airflow

Kubernetes

Apache Doris

What I fix

Five data problems
I get hired to fix

Data failures get more expensive the later they're discovered.

I add monitoring, data-quality checks and anomaly detection to your pipelines, so failures surface in minutes, not in Monday's executive report.

Observability & anomaly detection

Minutesfrom a broken pipeline to an alert, not days

Data-quality checksAirflow SLAsAlertingLineage

Your numbers live in ten systems and agree in none.

I consolidate sources into one governed analytical store with clear models and ownership, so every team reports from the same definitions.

Data platform architecture

Onesource of truth, modeled and documented

ETL / ELTApache DorisData modelingGovernance

Yesterday's batch can't drive today's decisions.

Where freshness matters, I move workloads to streaming: event pipelines and incremental views that keep dashboards and features seconds behind reality.

Real-time streaming

Secondsnot hours, from event to dashboard

RedpandaRisingWaveCDCMaterialized views

A model that never ships has all of the cost and none of the return.

I build the MLOps path from notebook to production: reproducible training, containerized serving on Kubernetes, CI/CD and drift monitoring.

MLOps & model serving

Repeatablea path to production, not a one-off heroic effort

KubernetesDockerCI/CDModel monitoring

LLMs are only as good as the data they can retrieve.

I prepare your knowledge for AI with clean ingestion, retrieval pipelines (RAG) and MCP tool access, so assistants answer from your data, with guardrails.

LLM & RAG systems

Groundedanswers traced back to your own sources

RAGMCPVector searchPython

01 / 05

My approach

I only fix what
I can measure

I measure what a problem costs before proposing anything. If the savings won't cover the cost of fixing it, I say so and stop there.

Baseline

I establish where the problem starts, what it affects and what it costs. You get the number and the method behind it.

Root cause

I identify what actually drives the problem: architecture, workload, cost, data quality or process. Until the cause is clear, implementation is guesswork.

Proof

I measure the change on your data, in your environment. The result, the method and the code stay with you.

The entry point

Start with one defined problem
and a measurable baseline

Begin with a focused assessment of one costly problem. Expand only if the findings justify it.

Platform diagnosis

Interviews and an architecture review, using production data only where the agreed scope requires it. You leave with a calculated cost, a ranked list of fixes and clear priorities.

Ask about a diagnosis

Measured pilot

One process or one source, with a working proof built in your own cloud or cluster. You keep the result, the method and reusable code. Implementation is included when the scope calls for it.

Ask about a pilot

About

Nine years of making
data boringly reliable

Data engineering and MLOps across a range of industries, from the first pipeline to AI in production.

Ali Fazeli · Tehran

I design and run the systems that move, model and serve data.

I'm skilled in data architecture, data modeling and ETL/ELT design, with deep Python and SQL expertise. I care about data quality and governance, so data stays reliable, consistent and secure long after the project is handed over.

9+years in data & AI infrastructure

2products founded and shipped

10+core platform technologies