# Noah Chalifour

Software engineer · British Columbia, Canada
chalifournoah@gmail.com
[linkedin.com/in/noahchalifour](https://linkedin.com/in/noahchalifour) | [github.com/noahchalifour](https://github.com/noahchalifour)

## Professional Experience

### Senior Software Engineer — Regie AI
*October 2025 - Present*

- Core engineer on Regie Go, a from-scratch AI sales agent product built for a V1 launch, owning the onboarding session state machine and SSE-driven progress engine powering a 12-stage first-run flow spanning company discovery, voice-profile learning, mailbox/phone connection, list enrichment, and AI drafting.
- Built the company-intelligence pipeline (BullMQ/Redis workers + real-time SSE) that reads a signup's email domain and auto-generates a company pitch, ICP, and voice profile for user confirmation, then parallelized draft generation and tuned job-queue concurrency to cut onboarding latency.
- Shipped the AI email drafting-to-send pipeline end to end, including citation-validated draft generation, an approve-all review UI, and a send service with suppression checks and CRM activity mirroring.
- Built an AI voice "welcome call" onboarding step on a server-signed ElevenLabs session (keys never leave the server) and a work-email signup gate backed by curated and vendored domain blocklists to restrict signups to verified business users.
- Drove sales-analytics accuracy on Regie's existing platform prior to the Go launch, building custom attribution logic for 12+ third-party CRM/dialer integrations (Avalara, Wellsky, Sitetracker, Alchemer, Bitdefender, and others) and shipping the Pipeline Visibility canvas feature end-to-end across frontend and backend, including a full v2 rebuild.

### Software Development Engineer II — Amazon Web Services (AWS)
*December 2024 - October 2025*

- Mentored an intern on native AWS system design and building production-ready machine learning services contributing to them successfully launching an LLM based AI documentation generator using AWS Lambda and Bedrock for keeping internal documentation up to date leading to a 50% decrease in manual effort required for maintaining documentation.
- Spearheaded the migration from a monolithic architecture to a scalable parent/child microservices model for our team, solely developing our dedicated child service leading to faster deployment cycles and increased approval speed by 75%.
- Implemented all necessary infrastructure for the new child service, successfully onboarding it to the parent service and contributing to a more modular and efficient system architecture resulting in a significant decrease in deployment time.
- Led our team's Zero QA Failures (ZQF) initiative, decreasing QA certification time by 80% through proactive quality improvements and test automation strategies.

### Software Development Engineer — Amazon Web Services (AWS)
*August 2022 - December 2024*

- Led project to clean up lingering RDS resources, freeing 10 petabytes of storage by implementing automated SQL scripts for safe identification and removal of unused resources without impacting customers.
- Co-developed backend for the Global Writer Endpoint feature in Java, enabling seamless database failover and switchover with no application changes needed, reducing downtime for customers by 40%.
- Guided 2 entry-level developers through onboarding program, resulting in successful integration into team projects.
- Developed internal tool using AWS Bedrock to leverage large language models such as Claude and Amazon Titan to compile developer artifacts and generate bullet point summaries for promotion documents in under 1 minute.

### Data Scientist Intern — Department of National Defence
*January 2021 - September 2021*

- Developed a prototype question-answering chatbot using NLP techniques such as BERT, designed to extract and summarize information from large-scale military datasets, demonstrating a 50% reduction in response time during tests.
- Published AI research paper *Question Answering Artificial Intelligence Chatbot on Military Dress Policy* describing techniques used for fine-tuning machine learning models for domain-specific language understanding, achieving 85.4% accuracy in question-answer matching.

### Software Developer Intern — The Co-operators
*May 2019 - January 2020*

- Migrated legacy quality control processes to a REST API using Node.js and Express, improving system flexibility and enabling seamless workflow integration leading to a productivity boost of 20%.

## Projects

### RNN-T Speech Recognition (rnnt-speech-recognition)
- Implemented Google's Streaming End-to-end Speech Recognition For Mobile Devices RNN-T deep learning model using TensorFlow and Keras, optimizing performance for real-time speech recognition on mobile devices.

### Py Micro (py-micro-service-template)
- Developed a reusable microservice template in Python utilizing gRPC, significantly streamlining the bootstrapping process for new microservices across development teams.

## Education

### University of Guelph — Guelph, ON
*September 2017 - April 2022*

- Major: Software Engineering, Bachelors of Computing
- Relevant Courses: Algorithms, Data Structures, Design, Object Oriented, Networks, Computational Intelligence, Graphics

## Skills

- **Programming Languages:** Java, Python, JavaScript, TypeScript, SQL, C++, Go (Golang)
- **Cloud Technologies:** Amazon Web Services (AWS) - Lambda, Bedrock, RDS, EC2, S3, Dynamo DB
- **Frameworks & Tools:** Spring, Flask, Node.js, Express, React, TensorFlow, MongoDB, Redis/BullMQ
- **Software Architecture & Design:** Distributed Systems, Microservices, Scalability, Concurrency, API Design, Event-Driven/Real-Time Systems (SSE, Job Queues)