Applied AI Engineer
- Montreal, QC, Canada
Full-time
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Job details
- Job type
- Full-time
- Apply by
- Posting language
- English
- Posted
Original job description
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.
More about this job
Responsibilities
Develop and integrate Generative AI applications, assistants, agents, and orchestration workflows with enterprise systems, data platforms, and APIs. Build prototypes and production-ready solutions, and evaluate their quality, safety, reliability, and performance while following enterprise security and data protection requirements.
Requirements
Requires 3–5 years of software engineering experience and 1–3 years developing Generative AI applications, with proficiency in Python and practical knowledge of LLMs, RAG, AI frameworks, and agentic application patterns. Candidates should also have experience with enterprise integrations, testing, cloud and CI/CD practices, and the ability to collaborate with business and development teams.
Skills
- Python
- Generative AI
- Large Language Models
- Prompt Engineering
- Retrieval-Augmented Generation
- Vector Databases
- Semantic Search
- LangGraph
- LangChain
- OpenAI SDK
- AI Agent Development
- API Integration
- Automated Testing
- CI/CD
- Cloud Platforms
- Responsible AI
Visa sponsorship
Not detected in the job text
Categories
- Software
- Technology
- Data & Analytics
- Engineering
Keywords
- Python
- Generative AI
- AI Assistants
- AI Agents
- Copilots
- Intelligent Automation
- 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 Orchestration
- Tool Calling
- Enterprise Applications
- API Integration
- Automated Testing
- Responsible AI
- AI Governance
- GitHub
- CI/CD
- Containers
- Azure
- AWS