Ahmed Baz
IT Infrastructure Engineer
Python
JavaScript
Node JS
Active 6 days ago
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Overview
Technical skills
Timeline
Roles
Overview
A backend API engineer (Middle) who builds pragmatic Flask-based REST services for business apps. The strongest proven skill is implementing REST endpoints with server-side business logic and DB interactions, as shown in baz-app/backend/routes/invoices.py and baz-app/backend/routes/quotes.py. The codebase lacks evidence of migration/versioning workflows, structured logging/observability, systematic testing, and production resilience patterns.
Technical skills
Languages
3
Python
JavaScript
Node JS
DevOps
16
AWS
Amazon S3
Amazon EC2
API Gateway
AWS Lambda
GitHub Actions
AWS Step Functions
Amazon CloudWatch
Amazon ECS
GitHub
Rest API
AWS Fargate
Docker
Git
Terraform
Linux
AI/ML
3
Claude
Claude Code
Anthropic
Other
12
Flask
Boto3
PostgreSQL
DynamoDB
CI/CD
WebSockets
IaC
CI/CD
Frontend
IAM
TCP/IP
OSPF
Timeline
Mansoura University
Bachelor's Degree •
Telecommunications & Computer Engineering
Middle Backend Developer
Confidence: Medium API Engineer
A backend API engineer (Middle) who builds pragmatic Flask-based REST services for business apps. The strongest proven skill is implementing REST endpoints with server-side business logic and DB interactions, as shown in baz-app/backend/routes/invoices.py and baz-app/backend/routes/quotes.py. The codebase lacks evidence of migration/versioning workflows, structured logging/observability, systematic testing, and production resilience patterns.
API Design
3/10
How well APIs are designed
Basic REST API design implemented with Flask blueprints and consistent JSON responses; missing explicit versioning, pagination, idempotency keys and richer error contract.
Evidence
baz-app/backend/routes/invoices.py: multiple GET/POST/PUT/DELETE endpoints with consistent JSON responses
baz-app/backend/routes/quotes.py: create_quote and get_quotes endpoints including input validation logic
baz-app/backend/utils/auth_middleware.py: token_required decorator applied to routes
Data Layer & Database
3/10
Working with databases
Hand-written parameterized SQL and RETURNING usage show practical DB work; no migration history, no explicit transaction isolation control, and no evidence of schema evolution tooling.
Evidence
baz-app/backend/routes/quotes.py: parameterized queries and INSERT ... RETURNING id
baz-app/backend/routes/invoices.py: transactional inserts into invoice_items and payments
baz-app/backend/utils/db.py: psycopg2-based get_db connector (load_dotenv present)
Scalability & Performance
2/10
Handling load and speed
Little evidence of deliberate scalability or performance engineering - no caching/invalidation, no queue-based decoupling, no connection pooling or load testing artifacts.
Evidence
baz-app/backend/routes/invoices.py: synchronous DB operations and item-by-item inserts without batching or caching
baz-app/backend/routes/quotes.py: sequential inserts for quote items and invoice conversion without queuing
System Architecture
3/10
Overall system structure
Reasonable module boundaries using Flask blueprints and a small utils layer; overall structure is monolithic rather than a distributed service decomposition and lacks secrets/config management best practices.
Evidence
baz-app/backend/app.py: imports and blueprint registration pattern
baz-app/backend/routes/*.py: feature-separated blueprints for invoices, quotes, reports, clients
Security & Auth
3/10
Protecting data and access
Shows security awareness via JWT middleware, authorization checks and parameterized queries; however secrets are hardcoded in places and token lifecycle, revocation, and stronger input validation are not evidenced.
Evidence
baz-app/backend/utils/auth_middleware.py: token_required decorator (JWT usage)
baz-app/backend/routes/quotes.py: verification of client ownership using company_id in queries
ahmdbaz/aws-employee-directory/app/app.py: hardcoded DB_HOST and DB_PASS in source
Reliability & Observability
2/10
Stability and monitoring
Basic reliability patterns exist (commit/rollback, try/except, finally closing connections) but there is no structured logging, correlation ids, timeouts, retry/backoff or metrics/observability shown.
Evidence
baz-app/backend/routes/invoices.py: try/except with conn.rollback and generic error returns
baz-app/backend/routes/quotes.py: try/except/finally blocks that close cursors and connections
Expertise
Python• Middle
Microservices & API Architecture• Middle
Industries
Commerce• Middle
Technologies
Rest API
Flask
Recommendations
- Use for developing RESTful backend services and business workflows (invoicing, quotes, clients) where rapid delivery and clear server-side logic are needed.
- Improve and maintain relational data models and SQL-based business logic, adding migrations and schema evolution tooling (Alembic or equivalent).
- Harden production-readiness: add structured logging, metrics, timeouts, retries with backoff, and secret/config management.
- Extend to small serverless integrations on AWS (Lambda + DynamoDB/S3) given existing boto3 usage in the repository.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
Middle Frontend Developer
Confidence: Medium App Engineer
Mid-level frontend web engineer specializing in hand-crafted, performance-conscious vanilla JavaScript UIs and interactive single-page experiences. The strongest proven skill is building real-time SPA interactions and resilient WebSocket flows as implemented in ahmdbaz/aws-trivia-game/frontend/index.html. There is little to no public evidence of automated tests, TypeScript or framework-level (React/Next) architecture and no backend application code or CI test coverage shown.
UI Component Architecture
4/10
How interface parts are built
Code shows deliberate UI rendering functions and modular DOM renderers but no framework or component system; composition is manual via small render functions.
Responsive & Cross-browser
5/10
Works on all screens and browsers
Responsive CSS and RTL support are implemented with media queries and dir=rtl; site adapts layout and touch/keyboard affordances are present.
Performance Optimization
5/10
Speed of the interface
Measured performance patterns and browser APIs are used: requestAnimationFrame, IntersectionObserver, efficient canvas drawing and limited retry loops for particle routing.
Accessibility & Semantics
3/10
Usable for everyone
Some accessibility and semantic work is present (aria attributes, keyboard Enter handling, focus management on join), but custom widgets lack full ARIA roles and broader a11y testing artifacts are absent.
State Management & Data Flow
4/10
Managing data in the app
Clear client-side state object and solid real-time data flow with WebSocket reconnection/backoff, timers, and guarded sends; no server-state cache invalidation, optimistic rollback or state-machine library present.
UX & Visual Polish
5/10
Look and feel quality
Strong visual polish and multiple UX states are implemented (toasts, reconnect overlay, reveal countdowns, confetti, pipeline animations), demonstrating attention to perceived performance and user feedback.
Expertise
Web Performance & Optimization• Middle
PWA & Web APIs• Middle
HTML & CSS• Middle
Industries
Commerce• Middle
Gaming• Middle
Technologies
Frontend
JavaScript
Node JS
WebSockets
IAM
Recommendations
- Build real-time multiplayer features, dashboards, or collaboration UIs that require WebSocket + UI state orchestration and graceful reconnection handling.
- Implement performance-first marketing sites or microsites with canvas/animation-heavy visuals and IntersectionObserver-driven reveals.
- Develop serverless frontends that integrate with AWS API Gateway/Lambda and include robust client-side state and UX states (loading, error, reconnect).
- Consolidate this work into a component library or small framework (TypeScript + lightweight components) and add automated tests and accessibility audits to raise engineering maturity.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
Middle DevOps Engineer
Confidence: Medium Cloud Architect
A Middle-level cloud engineer specializing in building AWS serverless pipelines and Terraform-based infrastructure. The strongest proven skill is designing event-driven order processing on AWS, evidenced by main.tf that wires Lambdas, Step Functions, SQS with DLQ and the lambda/orderSubmit.py handler which validates and enqueues orders. The public code lacks structured observability and alerting, remote Terraform state management and least-privilege IAM practices.
CI/CD Pipelines
3/10
Automated build and deploy
Basic GitHub Actions pipeline that deploys static content to S3 and invalidates CloudFront; functional but minimal, lacking reusable workflows, gating, caching strategies, or rollback steps.
Infrastructure as Code
4/10
Managing servers with code
Terraform defines a full serverless pipeline (Lambdas, Step Functions, SQS with DLQ, SNS, DynamoDB, API Gateway) and packages lambda code via archive_file; shows practical IaC usage but lacks remote state backend, modules, least-privilege IAM, and explicit drift/CI testing.
Containerization & Orchestration
Working with containers
Not evidenced in public code
Observability & Monitoring
Watching system health
Not evidenced in public code
Reliability & Incident Response
4/10
Keeping systems up
Some resilience patterns are present: SQS DLQ and redrive_policy, Step Functions with Catch states, and event source mapping configured; however there are no runbooks, postmortems, or sophisticated rollout/rollback automation.
Evidence
ahmdbaz/aws-terraform-order-pipeline/main.tf: aws_sqs_queue.orders_queue redrive_policy -> aws_sqs_queue.orders_dlq
ahmdbaz/aws-terraform-order-pipeline/main.tf: aws_sfn_state_machine.order_pipeline definition includes Catch blocks and explicit failure path
ahmdbaz/aws-terraform-order-pipeline/main.tf: aws_lambda_event_source_mapping.sqs_trigger with batch_size = 1
Cloud & Cost Optimization
3/10
Smart use of the cloud
Uses serverless, on-demand DynamoDB billing and managed AWS services which can reduce operational cost, but lacks explicit autoscaling strategies, rightsizing, spot/eviction plans, or cost-tracking measures.
Expertise
Cloud Platforms & Architecture• Middle
Industries
Commerce• Middle
Technologies
CI/CD
IaC
Python• Middle
GitHub Actions
AWS• since 2025
Boto3
AWS Lambda
Amazon EC2
Amazon S3
AWS Step Functions
API Gateway
Recommendations
- Build and harden additional CI pipeline features: reusable workflows, deployment gates, artifact signing, and rollback steps for safer production deploys.
- Add Terraform remote state with locking (S3 + DynamoDB), modularize common resources, and introduce infra tests (terratest or checkov/conftest) to improve drift detection and safety.
- Improve operational observability by adding CloudWatch alarms, OpenTelemetry or structured logging, dashboards as code and at least one SLO/alert with noise reduction and routing.
- Harden security posture: remove hard-coded credentials, adopt least-privilege IAM roles/policies and use secrets manager or workload identity for secrets.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories: