MSAI & Cloud Engineer
Mehdi Salhi portrait

AI & Cloud Engineer Crafting Intelligent Solutions

I'm Mehdi Salhi, an AI and Cloud Engineer with a passion for building AI-driven systems on AWS. Currently pursuing a Computer Science degree at SNHU, I integrate large language models into impactful

Work samples

Explore my work in AI and cloud engineering, showcasing projects that leverage AWS, large language models, and cutting-edge technologies.

  • Unkommon — Serverless AI platform on AWS
    Unkommon — Serverless AI platform on AWS

    Designed and deployed a serverless AI platform on AWS running a website chatbot and a voice agent on large language models via Amazon Bedrock. Built agentic, tool-calling workflows where the assistant checks availability and books appointments through function calls, using Python and Bedrock. Engineered the system prompt, tool schemas, and guardrails (including prompt-injection defenses), and added a Trie-based intent classifier to answer common questions before the LLM, cutting latency and per-call cost. Deployed on serverless AWS (Lambda, API Gateway, DynamoDB) as infrastructure as code with AWS SAM, with a CI pipeline and automated tests.

    Amazon BedrockLLMsPythonLambdaAWS SAM
  • Company Policy RAG — Retrieval-Augmented Generation system
    Company Policy RAG — Retrieval-Augmented Generation system

    Built a RAG pipeline with hybrid retrieval (dense embeddings + BM25) and cross-encoder reranking over proprietary documents. Evaluated retrieval quality with faithfulness and context precision, both 1.00 on a 10-question test set. Containerized with Docker and deployed with a FastAPI backend on Hugging Face Spaces.

    PythonRAGDockerFastAPI

About me

I'm Mehdi Salhi, an AI and Cloud Engineer with a passion for building AI-driven systems on AWS. Currently pursuing a Computer Science degree at SNHU, I integrate large language models into impactful

I'm an AI and Cloud Engineer, as well as an AWS Certified Solutions Architect Associate, focused on creating AI-driven systems on AWS. Currently, I'm a Computer Science student at SNHU, set to graduate in August 2026 with a 3.71 GPA. I've gained practical experience in integrating large language models into production applications, building retrieval-augmented generation pipelines, and designing workflows using Amazon Bedrock on serverless AWS infrastructure. I'm proficient in Python, LLM integr

Education

Southern New Hampshire University

B.S. in Computer Science · Class of 2026

Skills

Amazon BedrockLarge Language ModelsRAGHybrid SearchCross-Encoder RerankingAgentic / Tool-Calling WorkflowsPrompt EngineeringLLM EvaluationAmazon SageMakerLambdaAPI GatewayDynamoDBS3EC2IAMEventBridgeCloudWatchTerraformAWS SAMDockerGitHub ActionsCI/CDOIDCPythonBashTypeScript/JavaScriptSQLYAML

Unkommon — Serverless AI platform on AWS

Designed and deployed a serverless AI platform on AWS running a website chatbot and a voice agent on large language models via Amazon Bedrock. Built agentic, tool-calling workflows where the assistant checks availability and books appointments through function calls, using Python and Bedrock. Engineered the system prompt, tool schemas, and guardrails (including prompt-injection defenses), and added a Trie-based intent classifier to answer common questions before the LLM, cutting latency and per-call cost. Deployed on serverless AWS (Lambda, API Gateway, DynamoDB) as infrastructure as code with AWS SAM, with a CI pipeline and automated tests.

Amazon BedrockLLMsPythonLambdaAWS SAM
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Company Policy RAG — Retrieval-Augmented Generation system

Built a RAG pipeline with hybrid retrieval (dense embeddings + BM25) and cross-encoder reranking over proprietary documents. Evaluated retrieval quality with faithfulness and context precision, both 1.00 on a 10-question test set. Containerized with Docker and deployed with a FastAPI backend on Hugging Face Spaces.

PythonRAGDockerFastAPI
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AWS Cost Watchdog — Serverless FinOps tool on AWS

Built a serverless tool that uses ML-based anomaly detection to flag unusual AWS spend, with idle-resource detection and tag governance, surfaced in a React dashboard. Provisioned with Terraform (remote S3 state) and deployed via a GitHub Actions CI/CD pipeline using OIDC; four event-driven Lambda functions on EventBridge.

TerraformLambdaEventBridgePython
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✅ Verified by Extern

Wayfair n8n AI Agent Engineering Externship

Learn one of the most in-demand skills shaping the future of work: building AI agents. In this externship, you’ll step into the role of an AI Agent Developer and use n8n to design agents that think, plan, and act on real data — automating insights, decisions, and workflows. No coding experience required. By the end, you’ll create a live dashboard powered by your own AI agents, gaining hands-on experience that applies to any role or industry — from marketing and consulting to product management and data science.

AI Workflow DesignAI Agent DevelopmentPresentation SkillsWeb Scraping & Data Extraction

Overview

During my externship at Wayfair, I focused on developing AI agents using the n8n platform. This project involved designing and automating workflows to enhance data-driven decision-making. By the end, I created a live dashboard that showcased my AI agents' capabilities, equipping me with valuable skills relevant across various industries.

Wayfair n8n AI Agent Engineering Externship
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What I've accomplished

I produced three polished, trend-ready moodboard prompt sets, implemented a Prompt Node in n8n, and delivered usage instructions and prompts formatted for image generators.

Project breakdown

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