Applied AI Engineer - Canada
- Montreal, QC, Canada
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Original job description
Applied AI-Engineer Montreal (Canada)
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.
Required Qualifications
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.
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.
- 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.
More about this job
Responsibilities
Collaborate with business and technology teams to discover Generative AI use cases, build and test prototypes, and integrate AI capabilities into existing applications. Provide practical implementation support to application development teams and help deliver AI agents, orchestration workflows, and enterprise integrations.
Requirements
Requires 3–5 years of software engineering experience and 1–3 years of practical Generative AI application development experience, including proficiency in Python and enterprise application integrations. Candidates should understand LLMs, RAG, AI frameworks and agents, testing and evaluation, Responsible AI, modern software practices, and cloud platforms, with strong communication and collaboration skills.
Skills
- Python
- Large Language Models
- Prompt Engineering
- Retrieval-Augmented Generation
- Vector Databases
- Semantic Search
- AI Agents
- LangGraph
- LangChain
- API Development
- Enterprise Integrations
- AI Evaluation
- Responsible AI
- CI/CD
- Azure
Visa sponsorship
Not detected in the job text
Categories
- Technology
- Software
- Engineering
- Data & Analytics
- Finance & Accounting
Keywords
- Python
- Generative AI
- Large Language Models
- Prompt Engineering
- Embeddings
- Vector Databases
- Semantic Search
- Retrieval-Augmented Generation
- LangGraph
- LangChain
- Claude Code SDK
- OpenAI SDK
- Model Context Protocol
- AI Gateways
- Agent-to-Agent Communication
- AI Agents
- Agent Orchestration
- APIs
- Enterprise Applications
- Automated Testing
- AI Evaluation
- Responsible AI
- AI Governance
- GitHub
- CI/CD
- Containers
- Azure
- AWS
- Azure AI Foundry
- AWS Bedrock