Developing an Agentic AI-Powered Code Editor Part 2 - BC-958

Genre de projet: Innovation
Discipline(s) souhaitée(s): Génie - informatique / électrique, Génie, Informatique, Sciences mathématiques, Mathématiques
Entreprise: Farpoint Technologies Inc.
Durée du projet: 6 mois à 1 an
Date souhaitée de début: Dès que possible
Langue exigée: Anglais
Emplacement(s): Vancouver, BC, Canada
Nombre de postes: 2
Niveau de scolarité désiré: MaîtriseDoctoratNouvelle diplômée/nouveau diplômé
Ouvert aux candidatures de personnes inscrites à un établissement à l’extérieur du Canada: No

Au sujet de l’entreprise: 

Farpoint Technologies is a leading AI digital transformation consulting company that empowers top-tier organizations to build the AI-assisted workforce of the future. We specialize in providing consulting services to large public, private, and government entities, helping them create AI-accelerated workflows and innovative solutions. Our expertise includes leveraging LLMs, diffusion models, multimodal models, and executing special projects.

Veuillez décrire le projet.: 

This project aims to develop an agentic AI-powered code editor that autonomously analyzes, modifies, and optimizes entire codebases. Unlike traditional AI-assisted tools, this system proactively improves software structure, refactors multi-file projects, and enhances SQL performance without human intervention. Designed for enterprise and research environments, it integrates seamlessly into existing workflows, offering standalone and cloud-based deployment.

The core objective:

Agentic Code Transformation
• Develop an AI agent capable of autonomous, large-scale code edits while preserving software integrity.
• Implement context-aware refactoring to optimize maintainability and reduce technical debt.

Intelligent SQL Optimization
• Use schema-aware AI to autonomously generate and refine SQL queries for faster execution.
• Continuously learn and adapt to improve database efficiency.

Scalable, Autonomous Infrastructure
• Enable self-optimizing AI pipelines that scale across distributed environments.
• Deploy via Docker, Kubernetes, and cloud-native ML for real-time operations.

Interns will:
• Gain hands-on experience in prompt engineering and implementing the latest AI research.
• Train and fine-tune agentic AI models for self-driven code transformation.
• Develop algorithms for multi-file refactoring and SQL optimization.
• Build production-grade AI workflows, optimizing deployment with cloud-native AI infrastructure.
• Work with Next.js, React, and Electron, improving their proficiency in cross-platform application development.
• Develop cross-platform builds, ensuring seamless integration across multiple environments.
• Implement multithreading and parallel processing to optimize code execution speed and performance.
• Optimize memory management, caching, and concurrency for large-scale AI-driven code analysis.
• Improve network efficiency for distributed AI model inference and database interactions.
• Design fault-tolerant architectures to handle complex workflows and prevent system failures.

Expertise ou compétences exigées: 

• AI & Machine Learning:
• Basic understanding of AI/ML concepts, especially AI-driven automation.
• Familiarity with prompt engineering and fine-tuning AI models.

• Software Development:
• Experience with JavaScript/TypeScript, React, and Next.js.
• Knowledge of multi-file code refactoring and software optimization.
• Basic understanding of concurrency, caching, and performance tuning.

• Database & SQL:
• Exposure to SQL and query optimization concepts.
• Understanding of how databases interact with AI models.

• Cloud & Infrastructure:
• Familiarity with Docker and Kubernetes is a plus.
• Basic knowledge of cloud-based AI deployment and distributed systems.

• General Skills:
• Strong problem-solving and debugging abilities.
• Ability to work with large codebases and follow best coding practices.
• Eagerness to learn and adapt to new technologies.

• Nice-to-Have:
• Experience with Electron for cross-platform development.
• Understanding of AI-driven software workflows.
• Basic knowledge of fault-tolerant and scalable system design