Enterprise AI Platform Architect

This is a fully remote position, open to applicants in Canada.

📋 Description

• Oversee the comprehensive delivery of AI agent implementations.

• Perform architectural evaluations and offer suggestions for enhancing the performance, scalability, and security of the AI platform.

• Spearhead the design and deployment of production-level AI systems while actively contributing by writing impactful code. Expect to utilize Python, LangGraph, and LangFuse to bring concepts to fruition.

• Design intricate, stateful, multi-agent workflows using LangGraph. You will create the frameworks that enable autonomous agents to function with enterprise-level reliability and scalability.

• Advocate for AI observability by incorporating LangFuse for in-depth tracing, prompt versioning, and thorough evaluation. You will transform "black box" LLM interactions into clear and measurable performance data.

• Establish the engineering blueprints for the entire organization. You will create patterns for RAG architecture, advanced tool integration, context window optimization, and prompt engineering.

• As the organization’s internal scout for emerging technologies, you will evaluate new LLM providers and orchestration frameworks, ensuring our technology stack remains at the forefront of the agentic revolution.

⛳️ Requirements

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, or a related discipline.

• More than 7 years of experience in cloud computing architecture, software engineering, or technical consulting.

• At least 3 years of experience in AI/ML platform architecture and development, including substantial recent experience (2+ years) with generative AI and agentic architectures in production settings.

• Proven history of deploying AI applications to production environments, rather than just prototypes.

• Proficient in LangGraph — implementing stateful, cyclic, multi-agent workflows at an enterprise scale.

• Experienced with LangFuse - including tracing, evaluation, prompt management, and dataset-driven testing.

• Strong proficiency in Python and solid engineering fundamentals (testing, CI/CD, architecture).

• Experience with cloud AI deployment (AWS, Azure, or GCP), encompassing containerization and inference cost management.

• Knowledge of RAG architecture—vector databases, embedding models, and retrieval strategies.

🏝️ Benefits

• Competitive healthcare coverage.

• Wellness programs.

• Flexible time off for when you need it.

• Parental leave.

• Recognition programs.

• And much more!