Montassar Nawara

AI Systems Engineer

Engineering intelligent systems across Edge AI, Distributed Backend and Autonomous Agents

Edge Artificial IntelligenceAI Agents and Autonomous SystemsHigh Performance Backend EngineeringDistributed Systems
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Engineering Reliable Intelligent Systems

I build production-minded software where backend performance, AI decision layers, and hardware constraints are treated as one unified system. My work focuses on real-time behavior, measurable outcomes, and architecture that scales under pressure.

From Go APIs optimized for low-latency throughput to embedded ML inference deployed on ESP32, I approach engineering as a systems discipline: performance, reliability, and intelligence must evolve together, not in isolation.

Edge AI

Lightweight inference and DSP pipelines deployed on constrained devices.

Distributed Systems

High-throughput backend services with secure APIs and in-memory optimization.

Autonomous Agents

Agent pipelines combining reasoning, orchestration, and physical action.

Selected Systems

Khayyata Platform

Scalable full-stack marketplace connecting customers with local artisans through a Go backend and React frontend.

Deployed cloud infrastructure with low-latency delivery and production-ready API integration.

GoReactAWSCloudflareREST APIs
View repository

GOB Agent

Private

Embedded AI voice multi-agent pipeline combining STT, intent classification, LLM inference, and TTS on ESP32.

Designed offline-resilient command workflows and near real-time control for physical devices.

ESP32PythonLLM AgentsReactCapacitorAndroid
View repository

KINDR NLP

Real-time toxicity detection and rewriting pipeline with ML classification and LLM rewriting in one API call.

Integrated into a Chrome extension and React dashboard with live monitoring and analytics.

PythonFastAPIDetoxifyGroq/LlamaReactTypeScript
View repository

Aggression Detection AI

Audio classification pipeline from DSP feature extraction to embedded inference for verbal aggression detection.

Achieved around 82% F1-score and optimized inference for ESP32 memory and latency constraints.

scikit-learnAudio DSPMFCCESP32REST APIs
View repository

Core Expertise

Backend and Systems

GoFastAPIREST APIsHazelcastDistributed SystemsDocker

AI and ML

scikit-learnTensorFlow/KerasLLMsNLPAudio DSPMFCCComputer Vision

Web and Frontend

ReactTypeScriptTailwind CSSCapacitorChrome Extension

Embedded and IoT

ESP32ArduinoGPIOSensorsReal-Time Systems

Cloud and Workflow

AWSCloudflareLinuxGit/GitHubAgile/ScrumUML

Engineering Experience

Backend Development Intern

DotCom - Nabeul, Tunisia

Jul 2025 - Aug 2025
  • Built high-performance Go REST APIs backed by Hazelcast with response times under 5 ms.
  • Implemented API key validation and endpoint-level protection for secure production access.
  • Containerized the service stack with Docker and benchmarked concurrent load scenarios.
GoHazelcastDockerfasthttpGob Serialization

AI and Embedded Systems Research Intern

ENSI Research Laboratory - Tunis, Tunisia

Jun 2025 - Aug 2025
  • Designed an end-to-end audio ML pipeline with around 82% F1-score for aggression detection.
  • Built REST communication between inference services and embedded capture systems.
  • Optimized model inference for ESP32 memory and latency constraints.
Pythonscikit-learnAudio DSPMFCCESP32REST APIs

Academic Formation

Engineering foundation built around systems rigor, real-time constraints, and production-oriented intelligent architectures.

AI Systems Engineering Track - ENSI

2024 - Present

Engineering Degree in Computer Science, Manouba, Tunisia

Core Orientation

  • Systems-oriented engineering formation centered on real-time computing, software architecture, and algorithmic rigor.
  • Applied focus on intelligent systems: edge inference constraints, backend performance, and AI integration in production workflows.
  • Project-driven execution across embedded hardware, distributed services, and ML pipelines with measurable latency and reliability targets.

Preparatory Engineering Cycle

2021 - 2024

Preparatory Institute of Engineering of Nabeul, Nabeul, Tunisia

Core Orientation

  • Intensive scientific foundation in mathematics, physics, and analytical problem solving.
  • Quantitative base applied to optimization, signal processing, and machine learning system design.

Research and Publication

AI-Based Energy and Water Optimization for Sustainable Agriculture

Multi-model intelligent architecture for irrigation scheduling and energy optimization in smart agriculture systems.

Research Repository

Technical Learning Path

Continuous engineering learning across AI systems, production ML, scalable infrastructure, and agentic architectures.

Applied AI and ML Systems

Machine Learning FundamentalsAI FundamentalsPractical NLP and model-serving workflows

From model understanding to deployment-aware engineering decisions.

MLOps and Production Readiness

MLOps FundamentalsEvaluation and reproducibilityML lifecycle practices

Shipping maintainable AI services instead of notebook-only prototypes.

Infrastructure and Platform Engineering

Containerization and VirtualizationCloud deployment patternsScalable service operations

Runtime reliability, portability, and operational efficiency.

LLM and Agentic Architectures

RAG with OpenAI (Azure)Retrieval + generation integrationTask-oriented AI workflows

Controllable LLM systems connected to backend infrastructure.

Global Communication

International collaboration profile for technical teams.

  • Arabic - Native
  • English - Professional working proficiency
  • French - Professional working proficiency
  • Italian - Basic proficiency

Used for technical collaboration, documentation, and cross-functional communication in international engineering environments.

Let's Build Intelligent Systems Together

I am currently open to internship opportunities in AI systems, robotics, and distributed intelligent infrastructure.

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