Staff AI Builder

Robots & Pencils

. Lead the design and delivery of AI/ML systems .

Posted 11/5/2026full-timeRemote • CanadaLead💰 CA$176,612 - CA$243,680 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and delivering AI/ML systems, with a strong focus on agentic AI, RAG pipelines, and AWS technologies. Proficient in building reliable, scalable, and secure backend services while mentoring teams on best practices in AI-assisted development.

Highest-signal resume keywords

AI/ML System DesignAgentic AI DevelopmentRAG Pipeline ExpertiseAWS GenAI Stack ProficiencyPython and Node.js Development

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI ArchitectureModel DevelopmentIntegration Problem SolvingPrompt EngineeringProduction Reliability PatternsChunking StrategiesVector DatabasesEvaluation and ObservabilityFull-Stack DevelopmentRESTful APIs

Soft Skills

MentoringCollaborationProblem-Solving

Tools & Technologies

AWS BedrockDynamoDBDockerLangFuseLangSmithOpenSearchS3SQSEventBridgeStep Functions

Industry Keywords

GenAILLM SystemsAgentic EngineeringProduction LLM ObservabilityWorkflow Orchestration

Tech Stack

Tools & technologies

AWSCloudDockerDynamoDBJavaScriptNode.jsPython

About the role

Key responsibilities & impact

Lead the design and delivery of AI/ML systems
Define AI architecture and lead model development and optimization
Solve challenging integration problems with emphasis on reliability, security, and scalability
Design and build agentic workflows, including reasoning loops, tool/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 and Agent Core, including MCP-based tool design
Write and iterate on production system prompts
Build evaluation and observability into agents using golden datasets, RAGAS-style metrics, LLM-as-judge, and tracing
Design failure handling with retries, circuit breakers, fallback models, and user-facing degradation paths
Build Python and Node.js backend services and AWS serverless architectures
Design DynamoDB single-table schemas for conversation state, agent memory, and session history
Support event-driven orchestration with Step Functions, SQS, and EventBridge
Contribute to frontend integration points and write tested full-stack code
Support deployment, monitoring, and production troubleshooting in AWS and Docker environments
Partner with product, design, and delivery leads across global teams to scope and ship features
Mentor engineers on agentic engineering practices and AI-assisted development tools
Own features and releases end-to-end, including debugging, hardening, and production reliability

Requirements

What you’ll need

6+ years of professional software engineering experience, including meaningful time shipping GenAI/LLM-powered systems in production
Hands-on depth in agentic AI, including reasoning loops, tool/function calling, and multi-agent orchestration
Practical RAG expertise, including chunking strategies, embeddings, vector databases such as OpenSearch or similar, cosine similarity search, and re-ranking
Experience building evaluation and observability for LLM systems, including golden datasets, LLM-as-judge, RAGAS or comparable metrics, and tracing tools like LangFuse/LangSmith
Strong prompt engineering skills
Hands-on experience with AWS GenAI stack: Bedrock, Agent Core, Lambda, DynamoDB single-table design, S3, SQS, EventBridge, and Step Functions
Strong Python and Node.js skills, with experience building full-stack applications and RESTful APIs
Understanding of production reliability patterns for LLM-backed systems, including retries/backoff, circuit breakers, and fallback models
Experience with Docker and cloud-native deployment
Comfort owning ambiguous, integration-heavy problems and going deeper on technical answers
Helpful extras: Amazon Bedrock Agent Core, regulated or high-stakes domain experience, workflow orchestration tools, and production LLM observability tooling