Ahmed Gaiter

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Specialization & Engineering Principles

While frontier Large Language Models are inherently probabilistic, production systems wrapping them must be deterministic, observable, and resilient.

I focus on the core backend engineering required to make AI reliable at scale:

• Asynchronous API Gateways: Engineering non-blocking API gateways and full-duplex WebSockets using Python (AsyncIO) and FastAPI to maintain sub-second streaming latencies.
• Stateful Multi-Agent Workflows: Developing state machines via LangGraph, enforcing strict tool-calling boundaries, state persistence, and dynamic evaluation rubrics.
• Concurrency & Background Processing: Offloading compute-heavy inference, document parsing, and scoring jobs to Celery workers backed by Redis queues and PostgreSQL.
• Deterministic Validation & Safety: Guaranteeing API payload integrity with Pydantic v2 schemas, regex-compiled prompt injection filters, and multi-provider fallback routing (Groq ➔ Gemini ➔ OpenAI).

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Technologies & Tooling

Domain	Core Stack & Frameworks
Backend & Concurrency	Python (AsyncIO), FastAPI, Full-Duplex WebSockets, Celery, Redis (Pub/Sub & Caching)
AI & Agentic Systems	LangGraph, RAG Architecture, Tool-Calling Guardrails, LiteLLM, Whisper, Vision LLMs
Databases & Integrity	PostgreSQL, Alembic Migrations, SQLite, Pydantic v2 Strict Validation
Infrastructure & DevOps	Docker, Docker Compose, Kubernetes Manifests, GitHub Actions (CI/CD), Linux
Testing & Quality	Pytest, Async Fixtures, Mock LLM Test Suites, CI Workflow Automation

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Core Production Architectural Patterns

Instead of building brittle direct-API wrappers, my systems implement three proven production patterns:

1. Concurrency Decoupling Pattern

Problem: Running audio processing, LLM evaluation, and scoring inside an open WebSocket connection blocks the event loop and introduces 3–5 second candidate freezes.
Solution: The WebSocket router handles only streaming communication; transcript scoring and multi-metric evaluations are dispatched asynchronously to Celery workers backed by Redis.

2. Deterministic Agent Sandboxing

Problem: Autonomous LLM agents are prone to hallucinations, unauthorized operations, and schema drift.
Solution: Agents are modeled as LangGraph state machines restricted to verified, authenticated tool boundaries (get_shipment_status, verify_customer, cancel_shipment) with Pydantic output validation.

3. Multi-Provider Fallback Routing

Problem: Upstream LLM providers encounter rate limits (429), regional outages, or latency spikes during peak load.
Solution: Primary traffic routes to ultra-fast LPUs (Groq Llama/Qwen) with automated runtime fallback chains to Google Gemini Flash and OpenAI.

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Flagship Systems (Live & Open Source)

System	Architecture & Highlights	Tech Stack	Repository
AutoHire Autonomous Technical Interviewer	• Full-duplex WebSocket architecture in FastAPI for bi-directional streaming. • Event-driven LangGraph state machine calibrating questions dynamically. • Asynchronous transcript scoring via Celery workers backed by Redis. • Automated provider fallback (Groq ➔ Gemini) for uninterrupted uptime. • Full Pytest test suite covering WebSocket lifecycle and mock fixtures.	Python (AsyncIO) FastAPI WebSockets LangGraph Celery Redis Pytest	Live Demo GitHub Repo
Meridian Multimodal Document & Vocal Inspector	• Vision-language pipeline auditing blueprints and invoices via Llama 3.2 Vision. • Whisper voice integration with intelligent Voice Activity Detection (VAD). • Strict Pydantic v2 schema validation preventing model hallucinations. • Dockerized backend with full Pytest integration test suite.	Python FastAPI Llama 3.2 Vision Whisper Pydantic v2 React TypeScript	Live Demo GitHub Repo
Shiphny Multi-Agent Logistics Platform	• LangGraph cyclic state machine enforcing authenticated tool boundaries. • Decoupled background workflows with Redis sessions and PostgreSQL ledgers. • Automated multi-provider LLM fallback routing across Groq and Gemini. • Automated CI/CD pipeline building Docker images to GHCR with Helm charts.	FastAPI LangGraph PostgreSQL Redis Docker CI/CD Terraform Helm	Live Demo GitHub Repo
AI Guardrail API High-Throughput Safety Middleware	• Pre-flight prompt inspection catching injection and jailbreak patterns. • Automated regex-compiled PII redaction (Emails, Phones, Cards, API Keys). • Sub-5ms latency overhead engineered for enterprise API gateways. • Full Pytest test suite covering clean queries, injection attacks, and PII.	Python FastAPI Pydantic v2 Docker Regex Automata Pytest	GitHub Repo

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Professional Experience

• Software Engineer (AI & Backend) | Independent Contractor (Jan 2024 – Present)

  • Designed and deployed asynchronous RAG pipelines and multi-agent backend APIs using FastAPI and Python.
  • Implemented multi-provider LLM fallback routing to eliminate upstream rate limits and ensure service availability.
  • Architected asynchronous task queues backed by Redis to decouple heavy inference workflows from API response loops.

• AI Integration Engineer | Springer Capital (Remote Contract · May 2025 – Sep 2025)

  • Developed custom Python automation scripts and LLM endpoints for operational workflow synchronization.
  • Standardized data schemas across ingestion pipelines in collaboration with a remote engineering team.

• AI Alignment & Code Security Specialist | Atlas Capture (Remote Contract · Dec 2024 – Feb 2025)

  • Audited and debugged complex Python and TypeScript code generated by frontier LLMs for RLHF alignment benchmarks.
  • Evaluated prompt injection vectors and adversarial attack datasets to reinforce model response reliability.

• B.Sc. in Computer Science (Mansoura University, Egypt · 2019 – 2023)

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Contact

• Portfolio: ahmedgaiter.site
• LinkedIn: linkedin.com/in/ahmed-ai-dev
• Email: ahmedekramy303@gmail.com
• Location: Cairo, Egypt (Available for Remote worldwide & Relocation)