Applied AI Engineer - Canada

Saransh Inc; Montreal; On-site; CAD 90,000 - 150,000; Full time

Job summary

Morgan Stanley in Montreal seeks an Applied AI Engineer to accelerate Generative AI use cases across applications. You will collaborate with business and technology teams, build prototypes, test integrations and provide practical support to development teams.

The role requires solid software engineering, hands-on AI app development, and strong communication across functions. You will work with LLMs, AI frameworks, APIs, and enterprise platforms, while prioritizing quality, security, and

Qualifications

• 3 to 5 years of software engineering experience with hands-on application development responsibilities.

Skills

Python

API development

LLMs

Prompt engineering

AI agents

CI/CD

Azure/AWS

Communication

Problem solving

Tools

LangGraph

LangChain

Claude Code SDK

OpenAI SDK

MCP

Job description

Role Overview And Expectations

Applied AI engineers are hands-on developers who work collaboratively with business and technology teams to accelerate the implementation of Generative AI use cases across Morgan Stanley. Team members participate in discovery discussions, build and test prototypes, integrate AI capabilities with existing applications, and provide practical implementation support to sponsoring application development teams. Success in this role requires solid software engineering fundamentals, practical experience with AI application development, strong problem-solving skills, and the ability to communicate clearly and work effectively across teams.

Applied AI-Engineer Montreal (Canada)

Applied AI engineers are hands-on developers who work collaboratively with business and technology teams to accelerate the implementation of Generative AI use cases across Morgan Stanley. Team members participate in discovery discussions, build and test prototypes, integrate AI capabilities with existing applications, and provide practical implementation support to sponsoring application development teams. Success in this role requires solid software engineering fundamentals, practical experience with AI application development, strong problem-solving skills, and the ability to communicate clearly and work effectively across teams.

Required Qualifications

• 3 to 5 years of software engineering experience with hands-on application development responsibilities.
• 1 to 3 years of practical experience developing Generative AI applications, AI assistants, AI agents, copilots, or intelligent automation solutions.
• Proficiency in Python and experience building APIs, integrations, and enterprise applications.
• Working knowledge of Large Language Models (LLMs), prompt engineering, embeddings, vector databases, semantic search, Retrieval-Augmented Generation (RAG), and common AI application patterns.
• Hands-on experience with one or more AI development frameworks, such as LangGraph, LangChain, , Claude Code SDK, OpenAI SDK, or similar technologies.
• Familiarity with Model Context Protocol (MCP), AI gateways, agent-to-agent communication, or tool-calling frameworks.
• Experience implementing AI agents, orchestration workflows, tool integrations, or agentic applications.
• Experience building proofs of concept, prototypes, or reference implementations.
• Experience integrating applications with enterprise systems, data platforms, and APIs.
• Familiarity with automated testing and evaluation approaches for AI solution quality, safety, reliability, and performance.
• Awareness of Responsible AI principles, AI governance, security controls, and enterprise data protection requirements.
• Experience with GitHub, CI/CD pipelines, containers, and modern software engineering practices.
• Familiarity with Azure or AWS services used to host and operate applications and AI workloads.
• Ability to work with business stakeholders and application development teams to translate requirements into working software.
• Strong communication, collaboration, and problem-solving skills.

Preferred Qualifications

• Experience with Azure AI Foundry, AWS Bedrock, Anthropic Claude, OpenAI GPT models, SpaceX Grok Models, Gemini, or similar enterprise AI platforms.
• Experience building RAG solutions, knowledge assistants, or AI-powered search capabilities.
• Exposure to multi-agent systems and agent orchestration workflows.
• Experience integrating applications with ServiceNow, Jira, Confluence, SharePoint, Microsoft Graph, Databricks, Snowflake, or similar enterprise platforms.
• Familiarity with Kubernetes, containerized application deployment, and cloud-native architectures.
• Experience working in financial services or another regulated industry.