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 diagnosisYour 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.
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
01
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 detectionMinutesfrom a broken pipeline to an alert, not days
Data-quality checksAirflow SLAsAlertingLineage
02
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 architectureOnesource of truth, modeled and documented
ETL / ELTApache DorisData modelingGovernance
03
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 streamingSecondsnot hours, from event to dashboard
RedpandaRisingWaveCDCMaterialized views
04
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 servingRepeatablea path to production, not a one-off heroic effort
KubernetesDockerCI/CDModel monitoring
05
LLMs are only as good as the data they can retrieve.
I prepare your knowledge for AI — clean ingestion, retrieval pipelines (RAG) and MCP tool access — so assistants answer from your data, with guardrails.
LLM & RAG systemsGroundedanswers traced back to your own sources
RAGMCPVector searchPython
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.
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 — with implementation included when the scope calls for it.
Ask about a pilotAbout
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 · TehranI 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 — making sure 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
Python
Bash
Apache Airflow
Kubernetes
Docker
RisingWave
Redpanda
Apache Hadoop
Apache Doris
Selected work
Every project leaves
a number behind
Products I founded and built end to end — from ingestion and modeling to the interface people actually use.
Big data · BIZhaav — a big-data visualization platform
Founded and built a BI platform tailored to the Iranian market: ingestion, an analytical warehouse and interactive dashboards on top.
Founderarchitecture, data platform & product
Visit zhaav.net ↗
LLM · RAG · MCPZhaav SAS — a smart assistant system
An assistant that combines LLMs with retrieval (RAG) and MCP tool access to deliver seamless, context-aware help grounded in real data.
In productionretrieval, tools & serving
Visit sas.zhaav.org ↗
Open source
Open-source work on GitHub
Pipelines, tooling and experiments across data engineering, MLOps and LLM systems.
@alifzland the zhaav-bi organization
Browse repositories ↗
How I work
You can stop after the first stage.
I designed it that way.
Fixed-scope assessment
We start with a fixed-price assessment focused on one costly problem. After that, you decide whether the findings justify further work.
Decision based on evidence
If the findings don't justify more work, I stop — rather than recommend work the evidence does not support.
The knowledge stays with you
Code, documentation and know-how stay with your team. When the scope includes implementation, the code lives in your repository.
The cost of bad data rarely
appears on the balance sheet
It appears everywhere else in the business:
Wasted hours.
Engineering time spent babysitting pipelines, reconciling reports and fixing the same failures again.
Unexplained infrastructure costs.
Cloud and cluster spend that can't be clearly tied to workloads, owners or outcomes.
Customer impact.
Late or wrong information that slows decisions, frustrates customers and erodes trust in every dashboard.
Why does it matter? If your net margin is 3%, operational waste equal to 1 percentage point of revenue cuts your annual profit by a third.
My work starts by exposing that hidden invoice.
Because the most expensive decision is the one nobody made.