Assem Sohaib
Bensalah
AI Engineer · Founding Product Architect
PhD Candidate in Reinforcement Learning for Cyber-Physical Systems
Applied AI engineer with hands-on experience building and shipping LLM-powered products and agentic workflows. Strong background in Python, TypeScript, and distributed systems, with production experience in RAG pipelines, prompt engineering, tool-using agents, and AI-driven UX.
Proven ability to take AI systems from prototype to production, balancing quality, cost, latency, and operational reliability.
Selected Projects
Ziritex
Founding Product ArchitectArchitecting an AI-native collaborative platform for scientific writing, built around agent-based human–AI co-authoring workflows. Designed tool-using agents and prompt-chained generation for drafting, revision, and refinement, supported by an integrated RAG pipeline using vector embeddings and semantic search over document context. Applied structured outputs and feedback mechanisms to improve determinism, citation grounding, and writing quality.
Verbly
Founding AI / Mobile EngineerArchitected a production LLM-driven conversational system for real-time language proficiency assessment, supporting adaptive multi-turn interactions. Designed prompt strategies and stateful conversation orchestration for contextual coherence across long dialogues, plus an evaluation framework for conversation quality, contextual accuracy, and engagement. Integrated ASR and TTS pipelines with streaming LLM inference for end-to-end voice interaction at near real-time latency.
Rochemère Systems
Founding Product ArchitectEngineered a full-stack construction management platform end-to-end, from data model to client-facing UX. Designed a Supabase-backed schema with real-time data synchronization across concurrent on-site and office users. Built resource allocation and reporting modules to improve project transparency and stakeholder communication. Owned CI/CD, deployment, and observability on Next.js / Vercel.
AutopilotVA
AI EngineerDelivered an AI-powered virtual assistant SaaS automating a majority of routine business tasks for early customers. Built multi-agent workflows using CrewAI and custom Python services for orchestration, database retrieval, and report automation. Designed end-to-end UI/UX for chat, calendar, and analytics dashboards to improve task completion times.
Metrolabs
ML EngineerBuilt an NLP spam detection system on top of a fine-tuned BERT model, deployed as a Google Workspace add-on. Owned data preparation, model fine-tuning, and integration into the production add-on while leading a small cross-functional team. Defined evaluation metrics and held-out test protocols to validate model behavior before release.
Research & Applied AI
Conducting doctoral research in reinforcement learning for cyber-physical systems, with applications to power grid resilience and security. Published in PeerJ Computer Science with focus on deep reinforcement learning methods under adversarial and operational constraints.
A cascading policy learning framework for enhancing power grid resilience
Developed a three-stage cascading DRL framework (PPO → TRPO → A2C), achieving 84% success in maintaining 24-hour grid functionality under cyberattacks vs. 67% baseline (25% improvement).
PeerJ Computer Science · doi:10.7717/peerj-cs.3358
Open-Source Experimental Artifacts
Complete implementation, datasets, and reproducibility artifacts for cascading DRL framework. Demonstrated accelerated convergence and reproducible results across 100 attack scenarios.
Open Science Framework
An Artificial-Intelligence-based Approach to Enhance the Security of Cyber-Physical Systems
Doctoral research on reinforcement learning–based methods for securing and improving the resilience of cyber-physical systems under adversarial and operational constraints.
University of Oum El Bouaghi · Supervisor: Dr. Toufik Marir · Expected defense: April 2026
Technical Focus
Applied GenAI & Agentic Systems
- →LLM APIs (OpenAI, Anthropic, Google)
- →Multi-Agent Orchestration & Tool-Calling
- →RAG Pipelines (Vector Embeddings, Semantic Search)
- →Prompt Engineering (Structured Outputs, Chain-of-Thought)
- →LLM Evaluation (Quality, Latency, Cost)
- →Guardrails & Safety
AI/ML Research
- →Reinforcement Learning (PPO, TRPO, A2C)
- →Deep Learning (PyTorch)
- →Model Evaluation & Optimization
- →MLflow & Weights & Biases
- →Multi-Agent Systems
- →Simulation-based evaluation pipelines
Frameworks & Development
- →LangChain
- →FastAPI & Flask
- →Next.js & React Native
- →TanStack
- →RESTful APIs & WebSockets
Infrastructure & DevOps
- →GCP (Certified)
- →Docker & CI/CD
- →Supabase & Convex
- →Git & Vercel
- →Linux
Programming Languages
How I work
- ◆I favor simple systems that scale over clever abstractions
- ◆I care deeply about reproducibility and long-term maintainability
- ◆I optimize for clarity — in code, data, and interfaces
Contact
Interested in collaboration, research discussions, or just want to connect? I'd love to hear from you.