Publié il y a 2 mois
Senior AI Engineer
@ Urban Ridge Supplies
Brampton, Ontario
En bref
Charger Logistics Inc. is hiring a Senior AI Engineer based in its Brampton office to build production AI agents and MCP integrations that automate dispatch, billing, compliance and fleet operations. The role covers multi-agent workflows, RAG/KAG/CAG retrieval pipelines and deployment on Kubernetes. Full-time.
Responsabilités :
- Design and deploy MCP servers exposing domain services as AI tools
- Build multi-agent workflows for logistics automation
- Develop RAG, KAG and CAG knowledge retrieval pipelines
- Implement LLM integration layers and model routing
- Deploy and maintain agent infrastructure on Kubernetes
Résumé des exigences :
- 2-3 years of experience with a bachelor's degree in a technical field
- Production-grade Python AI application experience
- Hands-on LLM integration (function calling, structured outputs)
- Proficiency with SQL and an analytical data platform
- Experience with cloud platforms and Kubernetes
Outils et compétences
Source et dates
Site carrière / système de candidatures · teamtailor
- Date publiée par l’employeur
- 2026-09-24
- Première détection par notre source
- 2026-10-11
- Mise à jour du dossier source
- 2026-10-30
Ces dates décrivent le dossier source, pas une vérification en direct du recrutement. Consultez l’offre originale.
Détails du poste
| Niveau | Senior |
| Domaine | Développement logiciel |
| Expérience | 2 an(s) et plus |
| Heures par semaine | 40 (estimé) |
| Déplacements | avion : aucun · route : aucun |
| Milieu de travail | Bureau |
| Position | Surtout assis |
| Exigences physiques | physique faible · ordinateur élevé |
| Langues | English |
| Formation | Baccalauréat (exigé) |
| Domaines d’études | Computer Science, Artificial Intelligence |
Résumé et détails rédigés par l’IA à partir de l’offre; l’offre de l’employeur fait foi.
Urban Ridge Supplies
Profil d’entreprise sauvegardé
Renseignements historiques provenant d’un profil The Jobs API sauvegardé (champs LinkedIn/Crunchbase). Ils ne sont pas vérifiés en temps réel; les chiffres peuvent avoir changé. Les valeurs manquantes restent inconnues.
Supplying high-quality pipes for residential, commercial, and industrial needs.
Description complète de l’entreprise
Supplying high-quality pipes for residential, commercial, and industrial needs across Karachi. Fast delivery. Trusted quality.
- Abonnés LinkedIn
- 14 005
- Bureaux déclarés
- Sheikh Mohammed bin Rashid Boulevard 1, Zabeel, Dubai, AE
- Autres noms enregistrés
- Urban Ridge Supplies
- Le profil indique une agence de recrutement
- Non
Supplying high-quality pipes for residential, commercial, and industrial needs across Karachi. Fast delivery. Trusted quality.
| Champ | Valeur |
|---|---|
| Secteur | Wholesale Building Materials |
| Taille | 2-10 employees |
| Siège social | Zabeel, Dubai |
| Type d’organisation | Privately Held |
| Offres sur Jobsy | 124 |
| Voir la page |
Charger logistics Inc. is a world- class asset-based carrier with locations across North America. With over 20 years of experience providing the best logistics solutions, Charger logistics has transformed into a world-class transport provider and continue to grow.
We are looking for a highly motivated AI Engineer to join our team based out of our Brampton office and contribute to the development of AI-driven solutions for various departments. This role focuses on building production AI agents and MCP (Model Context Protocol) integrations that automate real logistics workflows—dispatch, billing, compliance, and fleet operations—improving the reliability, transparency, and efficiency of AI applications in real-world, high-stakes environments.
Responsibilities:
- Design, develop, and deploy MCP servers exposing domain services as AI-consumable tools with proper authentication, observability, and error handling.
- Build multi-agent workflows using orchestration frameworks and agent-to-agent communication protocols for complex logistics automation.
- Develop and optimize knowledge retrieval pipelines using RAG, KAG, and CAG strategies—selecting the right approach based on query complexity, data volatility, and domain reasoning requirements.
- Design hybrid retrieval architectures that route between CAG for static reference data, RAG for dynamic operational queries, and KAG for multi-hop reasoning across structured domain knowledge.
- Implement LLM integration layers—prompt engineering, function calling, structured output parsing, and model routing for domain accuracy.
- Collaborate with cross-functional teams to collect requirements and translate operational workflows into agent capabilities.
- Deploy and maintain agent infrastructure on Kubernetes with GitOps practices and observability tooling.
- 2-3 years of experience with Bachelor's in Computer Science, Artificial Intelligence, or a related technical field.
- Strong communication skills and experience working in interdisciplinary or team-based environments.
- Solid understanding of REST APIs, microservices architecture, and AI/ML concepts.
- Experience building production-grade AI applications in Python—not just notebooks or prototypes.
- Hands-on proficiency with LLM integration: function calling, tool use, structured outputs (OpenAI, Anthropic, or Google APIs).
- Solid understanding of knowledge retrieval patterns including RAG (Retrieval-Augmented Generation), with familiarity of emerging approaches like KAG (Knowledge-Augmented Generation) and CAG (Cache-Augmented Generation).
- Proficiency with SQL and at least one analytical data platform (BigQuery, Snowflake, or similar).
- Experience with cloud platforms and container orchestration (Kubernetes).
- Background in MCP, agent orchestration frameworks, knowledge graphs, or streaming data systems is a strong asset.
- Competitive Salary
- Healthcare Benefit Package
- Career Growth
Job Id: 3jmRdm88DDaLgjI21wlU5u5TYKV5q3R/L/vADFnL1ZYdxm8YqAc4HixPNkp73soOC7zoEyA8pRvnfAjkVs0/nmCSSxu8FNCI0w==
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