Staff AI Builder (AI)

Location: Remote in Canada

Salary: CAD 176,612–243,680 per year

Company

Robots & Pencils designs and delivers production-ready AI systems that integrate into enterprise operations and real-world workflows.

What you will do

• Design and build agentic workflows, including reasoning loops, tool and function calling, and single- and multi-agent orchestration.
• Build and maintain RAG pipelines with chunking, embeddings, vector search, re-ranking, and knowledge-refresh handling.
• Integrate AWS Bedrock, Agent Core, MCP-based tools, and production system prompts.
• Implement evaluation and observability using golden datasets, RAGAS-style metrics, LLM-as-judge, and tracing tools such as LangFuse or LangSmith.
• Build Python and Node backend services, serverless AWS architectures, DynamoDB schemas, and event-driven workflows.
• Own features and releases end-to-end, including deployment, monitoring, debugging, hardening, and technical mentorship.

Requirements

• 6+ years of professional software engineering experience, including production GenAI or LLM systems.
• Hands-on experience with agentic AI, RAG, prompt engineering, LLM evaluation, and observability.
• Experience with AWS Bedrock, Agent Core, Lambda, DynamoDB, S3, SQS, EventBridge, and Step Functions.
• Strong Python and Node.js skills, including full-stack applications and RESTful APIs.
• Knowledge of reliability patterns for LLM systems, including retries with backoff, circuit breakers, and fallback models.
• Experience with Docker, cloud-native deployment, and ambiguous integration-heavy engineering problems.

Nice to have

• Direct experience with Amazon Bedrock Agent Core, MCP tool wiring, memory and session management, and gateway provisioning.
• Experience in education, healthcare, financial services, or another regulated or high-stakes domain.
• Familiarity with Step Functions, Sequencer, or similar state-machine orchestration tools.
• Production experience with LLM observability tooling.

Culture & Benefits

• Remote work with collaboration across global teams.
• Production-ready AI systems are delivered on an accelerated 30–45-day cycle.
• Technical leadership, architecture ownership, and mentoring are central to the role.
• Engineering practices emphasize reliability, security, scalability, and measurable impact.