# Evidence

Stable pages that describe a claim, support, source, strength, and limitation.

## C++

Fayed uses C++ professionally in production and performance-sensitive systems.

Classification: demonstrated professional skill

Strength: Strong for professional C++ use in infrastructure and performance contexts.

## Python

Fayed uses Python professionally for automation, infrastructure, agents, ML systems, and production tooling.

Classification: demonstrated professional skill

Strength: Strong for Python in infrastructure, tooling, and agent workflows.

## Rust

Fayed has prior Rust experience and is interested in using Rust more heavily.

Classification: demonstrated prior professional skill and interest

Strength: Moderate to strong for Rust exposure in networking and infrastructure contexts.

## Production AI agents

Fayed has built production AI-agent workflows that operate against real production infrastructure.

Classification: demonstrated professional experience

Strength: Strong for production agent workflows tied to infrastructure operations.

## ML infrastructure

Fayed works with production ML infrastructure, ML ranking pipelines, ML serving, and operational debugging.

Classification: demonstrated professional experience

Strength: Strong for production ML infrastructure and operational systems.

## Distributed systems

Fayed has worked on production distributed systems operating at large scale.

Classification: demonstrated professional experience

Strength: Strong for production distributed-systems reasoning and operational debugging.

## Performance engineering

Fayed has professional experience with latency and systems performance.

Classification: demonstrated professional experience

Strength: Strong for practical production performance engineering.

## Developer tooling and programmable operations

Fayed builds tooling and APIs that make operational workflows programmable for humans and agents.

Classification: demonstrated professional experience and interest

Strength: Strong for infrastructure and operations tooling.

## Product engineering

Fayed is interested in moving closer to product and customer feedback loops and has independent product-building experience.

Classification: project experience and interest

Strength: Moderate for product-oriented engineering; strongest where product work intersects with infrastructure or AI.

## Startups and founding-engineer interest

Fayed is interested in founding-engineer and early-stage startup roles.

Classification: interest and inferred role fit

Strength: Moderate to strong for adjacent startup fit where systems, infrastructure, agents, or product ambiguity matter.

## Mathematics

Fayed studied Mathematics at the University of Texas at Austin.

Classification: education

Strength: Strong for mathematics education; adjacent for quantitative engineering interest.

## Quantitative engineering interest

Fayed is interested in quantitative finance and quantitative engineering as adjacent areas where mathematics, C++, performance, and systems experience may translate.

Classification: interest and inferred fit

Strength: Moderate as an adjacent-fit signal; not professional quant experience.

## Software engineering

Fayed has prior software-engineering experience at Amazon.

Classification: professional experience

Strength: Moderate to strong: employer, role type, and one feature claim are supported by canonical context; the public video supports the feature's existence.

## AWS Firewall Manager security group references

Fayed states that, during his Amazon / AWS internship, he built or materially contributed to the AWS Firewall Manager feature for referencing security groups in common security group policies.

Classification: professional experience with user-provided authorship and public feature documentation

Strength: Moderate: strong as user-provided canonical context for authorship; public video corroborates the feature name and existence, not individual authorship.