AI & Software Engineer

Mohammad El Prince

AI Engineer and Software Engineer building production AI applications and scalable, type-safe systems on AWS. I design retrieval-augmented and agentic systems, ship serverless platforms, and own engineering from architecture through production.

Mohammad El Prince

Currently Software Development at Amazon

2 AWS certifications: AWS Solutions Architect Associate · AWS Cloud Practitioner

  • 15+Serverless applications shipped
  • 100%Test coverage maintained
  • 2+Years of experience
  • 20+Technologies

Featured Projects

Quality Management Platform

Confidential
Quality Management Platform preview

Problem

Amazon fulfillment centers needed a scalable, multi-region platform to manage quality processes, track metrics, and provide real-time analytics dashboards for operations teams.

Solution

A TypeScript monorepo using React 19, tRPC, and AWS CDK to deliver a serverless API with CDN-based auth, analytics dashboards, and full infrastructure as code — deployed across multiple AWS regions.

Architecture

A TypeScript monorepo fronts a CDN that handles authentication and serves the React 19 web client. Client requests reach a type-safe tRPC API running on serverless compute, which reads and writes operational and analytics data. The entire multi-region footprint is provisioned and deployed through AWS CDK infrastructure as code, with 100% test coverage enforced across the stack.

Tech Stack

  • TypeScript
  • React
  • tRPC
  • AWS CDK
  • Serverless API

Challenges

  • Coordinating consistent quality data across multiple AWS regions.
  • Securing the API behind CDN-based authentication without adding latency.
  • Sustaining 100% test coverage across a growing TypeScript monorepo.

Results

  • Multi-region serverless platform serving operations teams in production.
  • 100% test coverage maintained across the full stack.
  • Infrastructure fully reproducible through AWS CDK as code.

AI Engineering

  • Retrieval-Augmented Generation

    Described competency

    Grounding language-model responses in external knowledge by retrieving relevant context at query time and feeding it into the prompt, reducing hallucination and keeping answers current with authoritative sources.

  • LLM Applications

    Described competency

    Designing production application flows around large language models — prompt design, streaming responses, context-window management, structured output, and guardrails — built on the same TypeScript and serverless foundations used for the platform work at Amazon.

  • Agents

    Described competency

    Composing autonomous and semi-autonomous agent workflows that plan, call tools, and iterate toward a goal, with explicit state, stopping conditions, and human-in-the-loop checkpoints for reliability.

  • Retrieval & Embedding Pipelines

    Described competency

    Building ingestion and embedding pipelines that chunk source content, generate vector embeddings, and index them for semantic retrieval — a natural extension of the serverless data-automation pipelines already shipped on AWS Lambda and EventBridge.

  • Tool Calling

    Described competency

    Exposing typed functions and external APIs to a language model so it can invoke them with validated arguments, mapping model intents onto real backend actions with the same type-safety discipline used for tRPC APIs.

  • Evaluation Pipelines

    Described competency

    Measuring model and application quality through automated evaluation harnesses — golden datasets, regression checks, and scoring metrics — applying the 100% test-coverage discipline maintained on the Quality Management Platform to non-deterministic systems.

Engineering Excellence

  • System Design

    Designing full-stack systems end to end — from edge authentication and type-safe APIs to data modeling and analytics — for the Quality Management Platform serving managers across North American fulfillment centers.

    • Designed a TypeScript monorepo (React 19, tRPC, AWS CDK) with a serverless API layer and CDN-based authentication.
    • Implemented edge-level authentication, role-based access control, and real-time user monitoring.
    • Built analytics dashboards with multi-location filtering and manager-level data aggregation.
    • AWS Lambda
    • CloudFront
    • DynamoDB
  • Scalability

    Building event-driven, serverless workloads that scale to production traffic across multiple locations without managing servers.

    • Developed 15+ serverless data-automation tools processing operational data from S3 and internal APIs.
    • Delivered real-time alerts via SES, SNS, and Slack to operations teams.
    • Launched the platform MVP at a pilot location and expanded toward 8+ additional fulfillment centers.
    • AWS Lambda
    • EventBridge
    • SES
    • SNS
    • S3
  • Cloud Architecture

    Architecting and deploying a multi-region web platform entirely as infrastructure-as-code, reproducible through AWS CDK with automated CI/CD.

    • Provisioned a multi-region footprint through AWS CDK infrastructure as code.
    • Owned the full lifecycle from architecture design through CI/CD deployment and production monitoring.
    • Configured CDN delivery, edge authentication, and observability for production workloads.
    • CDK
    • CloudFront
    • CloudWatch
    • Route 53
    • WAF
  • AWS Services

    Hands-on depth across the AWS serverless and infrastructure stack used to run the platform and data-automation tools in production.

    • Compute and APIs on AWS Lambda with tRPC and DynamoDB persistence.
    • Messaging and notifications through EventBridge, SES, and SNS.
    • Edge delivery and security via CloudFront, WAF, and Route 53, with monitoring through CloudWatch and RUM.
    • AWS Lambda
    • DynamoDB
    • S3
    • CloudFront
    • EventBridge
    • SES
    • SNS
    • CloudWatch
    • RUM
    • WAF
    • Route 53
  • Backend Engineering

    Writing type-safe, well-tested backend services in TypeScript and Python, owning the software lifecycle from requirements through production monitoring.

    • Built type-safe tRPC APIs backed by DynamoDB with role-based access control.
    • Maintained 100% test coverage across 6 packages using Vitest, ESLint, and snapshot testing.
    • Automated CI/CD pipelines for repeatable, infrastructure-as-code deployments.
    • AWS Lambda
    • DynamoDB

Experience

  1. 01/2019 – 02/2021

    Master of Engineering — Mechanical Engineering

    University of Guelph

    Course-based Master of Engineering in Mechanical Engineering in Guelph, ON.

  2. 2021 – 2024

    Operations

    Amazon

    Operations roles across multiple Ontario locations (Hamilton, St. Thomas, London, Mississauga) before transitioning to software development.

  3. 02/2023 – 10/2023

    Full Stack Web Development

    Flatiron School

    Full Stack Web Development online program in Python and JavaScript.

  4. 02/2025 – Present

    Data Analyst — Software Development Focus

    Amazon

    Building serverless applications and a multi-region quality management platform (React 19, TypeScript, tRPC, AWS CDK) serving managers at North American fulfillment centers.

  5. Present

    AI Engineering Focus

    Amazon

    Extending serverless and backend expertise into AI engineering — RAG, LLM applications, agents, retrieval pipelines, tool calling, and evaluation.

Skills

  • Backend

    • TypeScript
    • Python
    • Node.js
    • tRPC
    • Vitest
    • Git
    • Nx
    • ESLint
    • Prettier
  • Frontend

    • React 19
    • JavaScript
    • HTML/CSS
    • Vite
  • Cloud

    • AWS Lambda
    • CDK
    • S3
    • CloudFront
    • EventBridge
    • SES
    • SNS
    • CloudWatch
    • RUM
    • WAF
    • Route 53
    • Infrastructure as Code
  • AI

    • Retrieval-Augmented Generation
    • LLM Applications
    • Agents
    • Retrieval & Embedding Pipelines
    • Tool Calling
    • Evaluation Pipelines
  • Databases

    • DynamoDB
    • SQL

Certifications

  • AWS Certified Solutions Architect – Associate

    Amazon Web Services

  • AWS Certified Cloud Practitioner

    Amazon Web Services

Contact

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Open to AI Engineer, Software Engineer, and Backend Engineer roles. Reach out through any channel below.

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