AI Platform Engineer

Job Title: AI Platform Engineer Experience Level: Level 3 (senior): 5-7 years Location: Montreal (Day 1 onboarding onsite / in office presence 3x week) In the Technology division, we leverage innovation to build the connections and capabilities that power our Firm, enabling our clients and colleagues to redefine markets and shape the future of our communities. This is a AI Platform Engineering role at Director level, which is part of the job family responsible for providing specialist GenAI and expertise that drive decision-making and business insights during GenAI development as well as uplifting platform features by introducing cutting edge technology capabilities, enhancements and innovative solutions. Since 1935, Morgan Stanley is known as a global leader in financial services, always evolving and innovating to better serve our clients and our communities in more than 40 countries around the world. What you’ll do in the role: 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. What you’ll bring to the role: 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. 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. Experience with agent-based frameworks or orchestration systems Exposure to LLMOps / ModelOps / evaluation platforms Experience working in enterprise-scale platforms or internal developer platforms

Design and build a firmwide AI development and evaluation platform focusing on enterprise-scale GenAI benchmarking, assurance, and governance. Develop self-service tooling, SDKs, and APIs to enable the efficient and safe deployment of GenAI applications.

Requires 6+ years of software engineering experience in Python and cloud-native platforms, with at least 3 years specializing in GenAI and LLM applications. Proficiency in Kubernetes, GitOps, and data engineering principles is essential.

Identified from the job description. Confirm important requirements above.

• Python
• Kubernetes
• OpenShift
• GenAI
• LLM
• FastAPI
• Flask
• GitOps
• Vector Databases
• ModelOps
• LLMOps
• RESTful API
• Microservices
• CI/CD
• OAuth2
• OpenTelemetry

Minimum experience

5+ years

Apply by

Aug 27, 2026