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/Open

Shift using Git

Ops-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, Model

Ops/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 solid hands-on software engineering experience, preferably in Python (FastAPI, Flask),



building large-scale, cloud-native platforms.

Deep experience designing and operating Kubernetes/Open

Shift workloads using Helm, Customize, container registries, and Git

Ops 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 Dev

Ops, CI/CD, observability (Open

Telemetry, 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/Model

Ops/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