Summary

- Duration: 12 month

- Work Mode: Hybrid

- Location: Montreal

Responsibilities

- Design and build a firmwide AI development and evaluation platform with a strong focus on enterprise-scale GenAI benchmarking, assurance, and governance.

- Develop self-service tooling, SDKs, and APIs to enable teams to build, evaluate, and deploy GenAI applications efficiently and safely.

- Build reusable, scalable platform components for GenAI and agentic systems, including orchestration, evaluation pipelines, and model lifecycle workflows.

- Lead the implementation of container-native GenAI workloads on Kubernetes/OpenShift using GitOps-driven deployment patterns.

- Integrate and operate GenAI ecosystem components including LLMs, vector databases, embeddings, and agent frameworks.

- Drive key architecture, product, and design decisions across security, authentication, observability, scalability, and reliability.

- Establish platform best practices for GenAI evaluations, agentic systems, ModelOps/LLMOps, and production operations.

- Collaborate closely with engineers, data scientists, security, and product teams to accelerate safe enterprise adoption of GenAI.

Requirements

- Minimum 6 years of robust hands-on software engineering experience, preferably in Python (FastAPI,



Flask), building large-scale, cloud-native platforms.

- Deep experience designing and operating Kubernetes/OpenShift workloads using Helm, Customize, container registries, and GitOps practices.

- Minimum 3 years of experience building GenAI and LLM-based applications, including agentic orchestration, embeddings, evaluation workflows, and fine-tuning.

- Strong understanding of microservices, RESTful API design, asynchronous and concurrent programming, and performance-oriented systems.

- Solid foundation in data engineering principles including SQL/NoSQL stores, Kafka, Redis, vector databases, and state management at scale.

- Proficiency in DevOps, CI/CD, observability (OpenTelemetry, Prometheus, Grafana), and SRE-inspired operational practices.

- Strong working knowledge of security-first design, OAuth2, secure coding practices, and enterprise-grade platform controls.

Preferred Skills

- Experience with agent-based frameworks or orchestration systems.

- Exposure to LLMOps/ModelOps/evaluation platforms.

- Experience working in enterprise-scale platforms or internal developer platforms.

This role is for an existing vacancy.

📌 AI Platform Engineer (Montreal)
🏢 Axelon Services
📍 Montreal