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Worked as a remote SOC Analyst at CyberEdge Solutions (Australia): a managed security provider from November 2025 to February 2026. Operated across Splunk, Microsoft Sentinel, and Wazuh in a 24/7 production monitoring environment.
Key outcomes:
Reduced false positive rate by 25% through MITRE ATT&CK-aligned detection rule tuning.
Improved mean time to respond (MTTR) by 30% through Python and PowerShell automation.
Reduced log ingestion costs by 20% through optimised data collection.
Led end-to-end incident response for malware infections, unauthorised access, and cloud account compromise.
Maintained ISO 27001 compliance documentation and coordinated with engineering teams during high-severity incidents
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Proud to officially add the AWS Certified Machine Learning Engineer - Associate certification to my toolkit!
Building robust machine learning pipelines and scaling AI workloads securely on the cloud has always been a key focus of mine. Earning this certification has been a great deep-dive into mastering AWS ML infrastructure, MLOps best practices, and production-grade architectures.
Ready to bring these advanced capabilities to upcoming data science and engineering projects! Let's build something impactful. 💡💪
🔗 Verify Certificate:
Validation Number: 74c5808986a64b84addcaa9db725b8bd
Verification Link: https://aws.amazon.com/verification
#AWS #MachineLearning #AI #CloudComputing #DataAnalysis #MLOps
Congratulations on earning the AWS Machine Learning Engineer certification, Hazem khaled! Robust pipelines and cloud automation make all the difference for production-grade AI systems.
Most inbound bottlenecks aren't caused by low lead volume—they happen during triage.
When inquiries roll into a Google Sheet or CRM, operations teams often waste 10–15 minutes per submission reading through messy paragraphs, deciphering budget mismatch, and manually deciding who gets an immediate call vs. a standard rate sheet.
Instead of adding a bulky CRM add-on, I documented a lightweight 3-step automation pipeline built inside Make:
Intake & Extraction: An active listener triggers immediately upon a new submission, extracting core variables: client scope, target deliverables, timeline, and stated budget.
Context-Aware Evaluation (Gemini 2.5 Flash): The payload passes through an AI evaluation node with strict guardrails:
Evaluates budget against minimum agency criteria.
Assigns a triage score: Hot (immediate priority), Warm (nurture), or Cold (disqualified/template reply).
Generates a strict 15-word operational summary—no long paragraphs.
Determines an automated next step (e.g., routing to an account lead or flagging for asynchronous follow-up).
Data Return & Clean Pipeline: Appends the structured classification directly back to the master operational tracker, keeping the sheet filterable and clean.
The outcome: The sales and operations team can filter the entire board by priority status in two clicks, cutting first-touch response time to minutes without manually reading every single submission.
I’ve documented the full scenario map, execution logic, and data schemas below.
Curious how other agency operators and automation leads here handle their lead enrichment layer—are you doing AI scoring at the form level, or later in the CRM pipeline?:
For the budget gate, I'd keep an unstated budget separate from one explicitly below your minimum. Does the Make route ask for clarification when the amount or currency is missing, so a potentially suitable lead doesn't fall into Cold by default?
Vibe coding looks easy… until your app breaks. 😅
“Just tell AI what you want and it will build it.”
Sounds great.
Until you spend credits asking it to fix the same bug 10 different ways.
Then another feature breaks.
You fix that one.
Something else stops working.
Then you're staring at the same error for hours thinking:
“Why is this still happening?”
That's the part people don't show you about vibe coding.
AI can write code incredibly fast, but fast code doesn't automatically mean good code.
You still need to understand:
→ How the application is structured
→ Where the bug is actually coming from
→ How different features affect each other
→ When an AI-generated solution is making things worse
→ How to debug instead of blindly regenerating code
And sometimes the biggest time killer isn't building a feature.
It's getting stuck on one tiny bug that refuses to die.
That's why I don't see vibe coding as:
“Tell AI what to build.”
I see it as:
Human problem-solving + AI development.
The AI can help you move faster.
But you still need someone who knows where to go when things stop working.
That's where the real skill comes in.
What has been your biggest headache with vibe coding? 👇
— Optimum Studio
the same bug fixed 10 different ways is painfully accurate lol