Joseph Edaman Naja

Joseph Edaman Naja

AWS Certified: Solutions Architect – Professional · Associate · AI Practitioner

Paris-based infrastructure engineer moving into Solutions Architecture. Four years inside AWS operating the physical layer behind EC2 and GPU compute (P4d/P5) that large-scale ML workloads run on, after 18 months in a Tier III data center serving financial-services clients. I design scalable, cost-efficient AWS architectures informed by what actually happens beneath the API.

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Joseph Edaman Naja

Paris, France • josephnaja1@gmail.comLinkedInGitHubjosephnaja.com

Professional Summary

AWS Certified Solutions Architect – Professional, Associate, and AI Practitioner with 5+ years across data center operations and AWS infrastructure. Four of those years inside AWS in Paris, maintaining and recovering the EC2, EBS, S3, and GPU (P4d/P5) host fleet that large-scale ML training runs on, and leading operational and safety training for a six-person technician team. Before that, 18 months in a Tier III data center with a 100% uptime record on critical power and cooling for financial-services clients. Alongside that operational work I build AWS architectures end to end: multi-account landing zones, GPU inference platforms on EKS, and serverless application stacks. Now moving into a Solutions Architect role, turning infrastructure depth into designs that hold up in production.

Certifications

  • AWS Certified AI Practitioner | Apr 2026
  • AWS Certified Solutions Architect – Professional | Dec 2025
  • AWS Certified Solutions Architect – Associate | Mar 2023
  • AWS Certified Cloud Practitioner | Nov 2021
  • ITIL 4 Foundation | Jul 2021

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Core Competencies

Architecture & Design: AWS Well-Architected Framework, multi-account landing zones, high availability and disaster recovery, cost optimisation, serverless and event-driven patterns, multi-tier web applications
Compute & Containers: EC2, Auto Scaling, EKS, Kubernetes, Helm, Kustomize, Docker, ECS, Lambda, GPU node groups
Data & Storage: S3 (incl. Object Lock), EBS, EFS, RDS Multi-AZ, DynamoDB, Qdrant vector search
Networking & Security: VPC, Transit Gateway, Direct Connect, VPN, Route 53, ALB, CloudFront, IAM and IAM Identity Center, KMS, GuardDuty, Security Hub, Service Control Policies, DNS, IPv4/IPv6
AI/ML: LLM inference serving (vLLM), retrieval-augmented generation, embeddings and semantic search, GPU-accelerated workloads, model evaluation gating
Automation & IaC: Terraform, CloudFormation, GitHub Actions, Python, Bash, Git, GitOps and canary deployment pipelines
Observability & Operations: CloudWatch, Prometheus, Grafana, incident response and root cause analysis, ITSM/ITIL, hyperscale and Tier III data center operations

Professional Experience

Amazon Web Services (AWS)

Data Center Technician

Paris, France May 2022 – Present
  • Maintain and recover EC2, EBS, and S3 host fleets, including P4d and P5 GPU capacity used for large-scale ML training, returning hardware to service against strict availability targets.
  • Install, configure, and troubleshoot compute and network racks across the physical compute, storage, and networking layers that EC2, EBS, S3, and VPC are built on.
  • Drove on-site diagnosis of an inter-site DWDM metro span failure that removed link redundancy between two data centers, coordinating with optical on-call engineers, the fiber vendor, and the colocation provider until the fault domain was isolated and redundancy restored.
  • Lead operational and safety training for a six-person technician team and produce the site's monthly operations reporting.
  • Contribute to post-incident reviews with network engineering and vendor teams, feeding findings back to the service teams that own the affected systems.

Correlation One (Amazon Career Choice)

Software Developer Fellow

Remote Jul 2024 – Oct 2024
  • Selected for a competitive 23-week full-stack fellowship covering JavaScript, React.js, RESTful APIs, PostgreSQL, and HTML/CSS.
  • Built and deployed multi-tier web applications, including an e-commerce platform with a relational database backend, across frontend, backend, and data layers.
  • Delivered projects against weekly milestones with code review and stakeholder demos.

MainOne Cable Co. Ltd

Data Center Operations Engineer

Accra, Ghana Dec 2020 – May 2022
  • Held 100% uptime across all critical electrical and precision cooling systems (UPS, PDU, CRAC, feeders, transformers) in a Tier III data center serving financial-services clients.
  • Racked, installed, configured, and troubleshot 100+ servers, storage arrays, and network devices.
  • Ran incident and problem management in Remedy (ITSM), including structured root cause analysis and change control.
  • Produced daily, weekly, and monthly availability reporting against client SLAs.
  • Supervised facility and IT vendors on site, enforcing documented procedures and operational checklists.
  • Produced audit-ready documentation for internal reviews and external compliance assessments.

National Communications Authority (NCA)

Graduate Engineer

Accra, Ghana Sep 2019 – Aug 2020
  • Audited national frequency allocations and proposed a reallocation that was adopted, recovering usable spectrum from idle assignments.
  • Authored technical reports used in regulatory decisions on mobile data service quality.
  • Worked with cross-border counterparts on frequency-sharing agreements.

Cloud Architecture Projects

Self-directed builds, designed and implemented end to end. Code and write-ups on GitHub.

AI Model CI/CD Pipeline (MLOps)

An MLOps CI/CD pipeline for deploying LLM models on AWS EKS with automated canary deployments and rollback. Three-stage workflow: CI (lint, test, model evaluation against quality thresholds), staging deployment with smoke tests, and production canary rollout with Prometheus-based health monitoring. Terraform provisions ECR, S3, and IAM with GitHub OIDC for keyless auth; Kustomize handles environment overlays; GitHub Actions orchestrates. A model evaluation framework gates releases on p95 latency, throughput, quality score, and error rate.

Mistral RAG Assistant

A retrieval-augmented generation application powered by Mistral AI, with semantic search over Qdrant, multi-format document ingestion (PDF, DOCX, TXT, MD), recursive chunking with overlap, streaming chat with source citations, and an evaluation framework measuring retrieval precision, answer relevance, faithfulness, and correctness. Containerised with Docker Compose and deployed to AWS ECS via GitHub Actions.

Mistral LLM Inference Platform on AWS EKS

An LLM inference platform serving Mistral 7B Instruct on AWS EKS with vLLM on NVIDIA A10G GPUs (Spot instances for 60–70% cost savings). A FastAPI gateway provides bearer-token auth and per-IP rate limiting; autoscaling runs on a custom HPA metric (pending inference queue depth); Terraform provisions VPC, EKS, ECR, and managed node groups; Helm handles deployment; Prometheus and Grafana provide observability; CI/CD implements canary rollout with automated smoke tests and rollback. Load-tested with Locust at a 0% error rate and ~3.4 s p50 latency for 100-token completions.

Secure Multi-Account AWS Landing Zone for Banking

An AWS Landing Zone for French banking institutions subject to DORA and GDPR. Seven dedicated accounts across four OUs, centralised security monitoring, immutable audit logging (365-day S3 Object Lock), hub-and-spoke networking via Transit Gateway with route table segmentation, dual-path hybrid connectivity (Direct Connect plus VPN), and cross-region disaster recovery designed for RTO 4 h / RPO 1 h. Six Service Control Policies enforce region restriction, encryption, and service allowlisting. Codified in 10 reusable Terraform modules with a simulation mode for single-account testing.

AWS Cloud Resume Challenge

A full-stack serverless website on S3 (static hosting), CloudFront (CDN with ACM/HTTPS), Route 53 (DNS), DynamoDB (visitor counter), Lambda (Python API backend), and API Gateway. Infrastructure defined in CloudFormation; GitHub Actions syncs updates to S3 and invalidates the CloudFront cache on every push to main. This site runs on it.

WordPress High-Availability Deployment

A highly available WordPress deployment across two Availability Zones: public and private subnets in a custom VPC, an Application Load Balancer fronting EC2 instances in an Auto Scaling group, RDS Multi-AZ for the database, EFS for shared media, NAT Gateways for outbound access from private subnets, Route 53 for DNS, and ACM for TLS.

Education

Abdelmalek Essaadi University

MSc: Telecommunication Engineering

Tétouan, Morocco2019

Hassan I University

BSc: Telecom and Computer Networks Engineering

Settat, Morocco2017

Languages

English (fluent) • French (fluent)

Hands-On Projects

AI Model CI/CD Pipeline (MLOps)

GitHub ActionsTerraformEKSECRDockerKustomizeCanary DeployPrometheusPythonvLLMS3IAM OIDC

Built a complete MLOps CI/CD pipeline for deploying LLM models on AWS EKS with canary deployments and automated rollback. The pipeline features a three-stage workflow: CI (linting, unit tests, model evaluation against quality thresholds for latency, throughput, and accuracy), staging deployment with smoke tests, and production canary rollout (10% → monitor → promote or rollback). Infrastructure is managed via Terraform (ECR, S3 with lifecycle policies, IAM with GitHub OIDC for keyless auth). Kubernetes manifests use Kustomize overlays for environment-specific configs. An automated model evaluation framework compares each deployment against baseline quality scores, gating production releases on p95 latency < 5s, throughput > 50 tokens/sec, and error rate < 1%. Includes a version tracker storing deployment history in S3 for audit and rollback.

📖 Read blog post →

Mistral RAG Assistant

FastAPIReactQdrantMistral AIDockerTypeScriptRAGVector DBLLMGitHub Actions

Built a production-ready Retrieval-Augmented Generation application that lets users upload documents (PDF, DOCX, TXT, Markdown) and ask questions with source-cited answers. The system features recursive document chunking with overlap, batch embedding via Mistral's mistral-embed model (1024 dimensions), cosine similarity search over Qdrant, and RAG-augmented generation with streaming responses. Includes a built-in evaluation framework measuring retrieval precision, answer relevance, faithfulness, and correctness. The React frontend provides a chat interface with collapsible source panel, drag-and-drop document upload, and an evaluation dashboard. Fully containerized with Docker Compose and CI/CD via GitHub Actions to AWS ECS.

📖 Read blog post →

Mistral LLM Inference Platform on AWS EKS

EKSTerraformHelmvLLMGPU (A10G)FastAPIPrometheusDockerGitHub ActionsSpot InstancesHPA

Designed and deployed a production-grade LLM inference platform serving Mistral 7B Instruct on AWS EKS with GPU-accelerated inference via vLLM. The architecture features a FastAPI gateway with bearer token authentication and per-IP rate limiting, proxying OpenAI-compatible requests to a vLLM backend running on NVIDIA A10G GPUs (Spot instances for 60-70% cost savings). Infrastructure is provisioned via Terraform (VPC, EKS, ECR, managed node groups with AL2023 NVIDIA AMI), deployed with Helm charts, and monitored with Prometheus metrics and Grafana dashboards. Autoscaling is driven by a custom HPA metric (pending inference request queue depth). CI/CD pipelines implement canary deployments with automated smoke tests and rollback. Load testing with Locust validated 0% failure rate at ~3.4s p50 latency for 100-token completions.

Mistral LLM Platform Architecture Diagram
📖 Read blog post →

AWS Cloud Resume Challenge

S3CloudFrontRoute 53DynamoDBLambdaAPI GatewayCloudFormationGitHub Actions

Built and deployed a full-stack serverless personal website on AWS. Static files are hosted in S3 and served globally through CloudFront with a custom domain and HTTPS via ACM. A visitor counter is powered by a Lambda function (Python) backed by DynamoDB, exposed through API Gateway and routed via CloudFront. The entire infrastructure is defined as code using CloudFormation and deployed with a single script. A GitHub Actions CI/CD pipeline automatically syncs website updates to S3 and invalidates the CloudFront cache on every push to main.

AWS Cloud Resume Challenge Architecture Diagram
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Secure Multi-Account AWS Landing Zone for Banking

TerraformAWS OrganizationsTransit GatewayKMSSecurity HubGuardDutyCloudTrailDirect ConnectS3 Object LockIAM Identity Center

Designed and implemented a production-grade AWS Landing Zone for French banking institutions subject to DORA and GDPR regulations. The architecture provisions seven dedicated accounts across four OUs, with centralized security monitoring, immutable audit logging (365-day S3 Object Lock), hub-and-spoke networking via Transit Gateway with route table segmentation, dual-path hybrid connectivity (Direct Connect + VPN), and cross-region disaster recovery designed for RTO 4 h / RPO 1 h. Six Service Control Policies enforce compliance guardrails including region restriction, encryption enforcement, and service allowlisting. The entire infrastructure is codified in 10 reusable Terraform modules with a simulation mode for single-account testing.

AWS Landing Zone Organizations Architecture
📖 Read blog post →

WordPress Application Deployment

VPCEC2RDSEFSALBAuto ScalingRoute 53ACMNAT Gateway

Designed and deployed a highly available WordPress application for a medium-sized business on AWS. The architecture spans two Availability Zones with a VPC containing public and private subnets. An Application Load Balancer distributes traffic to EC2 instances running in an Auto Scaling group across private subnets. Amazon RDS (Multi-AZ) provides a managed, fault-tolerant MySQL database, while Amazon EFS provides shared storage for WordPress media files across all instances. NAT Gateways in the public subnets enable outbound internet access for the private instances. Route 53 handles DNS routing and ACM provides HTTPS encryption.

WordPress Application Deployment Architecture Diagram

Gen AI Spending Analyst Agent

Amazon BedrockConverse APIClaude 3Tool UsePythonMatplotlib

An AI-powered spending analysis agent built with Amazon Bedrock's Converse API and the tool use pattern. The agent reads transaction data from CSV files, categorizes expenses using keyword matching, calculates budgets with date-range filtering, and generates visual charts (pie and bar) — all through natural language conversation. The CLI-based agent uses a multi-turn conversation loop where Claude decides which local Python tools to call based on the user's question, executes them locally, and interprets the results in plain English. Built as a learning project to explore the fundamentals of building AI agents with function calling.

Certifications

AWS Certified AI Practitioner badge

AWS Certified AI Practitioner

April 2026

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AWS Certified Solutions Architect – Professional badge

AWS Certified Solutions Architect – Professional

December 2025

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AWS Certified Solutions Architect – Associate badge

AWS Certified Solutions Architect – Associate

March 2023

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AWS Certified Cloud Practitioner badge

AWS Certified Cloud Practitioner

November 2021

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ITIL 4 Foundation badge

ITIL 4 Foundation

July 2021

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