AI Engineer

Job summary

Design and build maintainable Python applications, APIs, and services that integrate LLMs, enterprise data, and business tools. Develop stateful LangGraph agent workflows, engineer their context, and use evaluation and observability findings to improve deployed applications.

Job details

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.

What you’ll do

Design and build maintainable Python applications, APIs, and services that integrate LLMs, enterprise data, and business tools. Develop stateful LangGraph agent workflows, engineer their context, and use evaluation and observability findings to improve deployed applications.

Requirements

Requires strong Python software engineering experience and demonstrated hands-on implementation of LangGraph agents integrated with external tools or services. Candidates should understand LLMs, RAG, retrieval and context engineering, agent evaluation, production delivery, security, and collaboration with business and engineering stakeholders.

Listed skills

• Python · Preferred
• prompt engineering · Preferred
• Automated testing · Preferred
• CI/CD · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Python
• LangGraph
• AI Agent Development
• Large Language Models
• Asynchronous Programming
• API Development
• Retrieval-Augmented Generation
• Embeddings
• Vector Search
• Context Engineering
• Prompt Engineering
• Agent Memory Management
• LLM Evaluation
• LLM Observability
• Automated Testing
• CI/CD

Job areas

• Technology
• Software
• Data & Analytics
• Finance & Accounting

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