Applied AI Engineer

Summary:

• Location: Montreal
• Work Mode: Onsite/in-office presence required 3x/week
• Duration: 12 months

Responsibilities:

• Collaborate with business and technology stakeholders to understand Generative AI use cases and translate requirements into practical implementations.
• Develop proofs of concept, prototypes, and reference applications that demonstrate business value and accelerate AI adoption.
• Build and enhance AI-powered applications, copilots, assistants, and agentic workflows using the company’s enterprise AI platforms and services.
• Implement Retrieval-Augmented Generation (RAG) solutions using enterprise knowledge repositories, vector databases, embeddings, semantic search, and retrieval techniques.
• Develop AI agents and orchestration workflows using frameworks such as LangGraph, LangChain, Semantic Kernel, Claude Code SDK, OpenAI SDK, or similar technologies.
• Implement prompt engineering techniques, structured outputs, tool calling, context management, and workflow orchestration patterns.
• Build APIs, connectors, and integrations between AI applications and enterprise systems, data platforms, and business workflows.
• Create reusable components, templates, implementation patterns, and developer examples that help application teams deliver AI solutions more efficiently.
• Develop automated tests and evaluation capabilities to measure solution quality, grounding, reliability, safety, and performance.
• Troubleshoot implementation issues and support application teams as prototypes and reference solutions transition toward production.
• Collaborate with firmwide AI platform teams to use foundational AI capabilities and follow enterprise engineering, security, governance, and Responsible AI standards.
• Stay current with emerging AI technologies and apply relevant tools and techniques to business use cases.

Requirements:

• Minimum 3 years of software engineering experience with hands-on application development responsibilities.
• Minimum 1 year 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 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.

This role is for an existing vacancy.