Currently an Extern @Wayfair
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
Works samples
Explore my work in AI and cloud engineering, showcasing projects that leverage AWS, large language models, and cutting-edge technologies.
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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
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·
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
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
Externships
Wayfair n8n AI Agent Engineering Externship
Wayfair
Education
Southern New Hampshire University
B.S. in Computer Science · Class of 2026
Skills
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.
Overview
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.
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.
Overview
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.
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.
Overview
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.