AI Platform Engineer
Summary
Build and run a firmwide GenAI platform: design Kubernetes-native tooling, SDKs, and evaluation pipelines so teams can safely build, deploy, and govern enterprise-scale AI applications.
- 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.
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 strong 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.