Job Title: AI Engineer
Job Type: Contract
Duration: 3 months (Possibility of extension)
Location: Montreal (Hybrid)
Language: English (Mandatory) French (Strongly preferred)
Job Description:
- Design and build modular, maintainable Python applications, APIs, and services that integrate LLMs, enterprise data, and business tools.
- Develop LangGraph-based agent workflows with explicit state, conditional routing, tool execution, checkpointing, and human-in-the-loop approval or escalation where appropriate.
- Engineer the context supplied to agents: select and assemble relevant instructions, retrieved information, tool outputs, and conversation history within token and access-control constraints.
Required skills and experience
- Strong hands-on Python software engineering, including application design, asynchronous programming, APIs, automated testing, error handling, and maintainable code.
- Demonstrated experience implementing AI agents with LangGraph, including stateful orchestration and integration with external tools or services - not only chatbots, framework tutorials, or conceptual designs.
- Practical understanding of LLMs, embeddings, retrieval-augmented generation (RAG), vector search, and the tradeoffs involved in retrieval and context assembly.
- Hands-on experience with context engineering, prompt engineering, and agent memory management.
- Experience designing and implementing evaluation frameworks for LLM or agent applications, including test-set construction and interpretation of evaluation results.
- Experience with Galileo or a comparable LLM/agent evaluation and observability platform, with the ability to trace failures and use findings to improve applications
- Experience delivering and supporting deployed applications, with sound understanding of security, reliability, version control, CI/CD, and production troubleshooting.
- Ability to explain technical decisions, distinguish personal contributions from team outcomes, and collaborate effectively with business and engineering stakeholders.
Highly Desirable Experience
- Knowledge of Model Risk Management, model development/validation lifecycles, model documentation, governance, or model monitoring.
- Experience in broader risk management, particularly credit risk, market risk, or financial-services risk workflows.
- Experience with Snowflake, Databricks, lakehouse architectures, enterprise data pipelines, or governed knowledge integration.
- Familiarity with Model Context Protocol (MCP), cloud deployment, containers, and enterprise identity/access controls.
- Prior experience using AI coding assistants such as GitHub Copilot, Claude Code, or Amp, with disciplined review, testing, and secure handling of code and data.
