Full-Stack & Platform Engineering

• Build backend services in Python and Node.js, including serverless architectures (AWS Lambda, API Gateway) that support agentic workflows
• Design DynamoDB single-table schemas — composite keys, transactional writes — for conversation state, agent memory, and session history
• Support event-driven orchestration (Step Functions, SQS, EventBridge) for asynchronous agent operations, with an eye toward reliability and clean error handling
• Contribute to frontend integration points so agentic features land cleanly for end users, and write clean, well-tested code across the stack
• Support deployment, monitoring, and production troubleshooting in cloud-native environments (AWS, Docker), with guidance from senior engineers where needed

Collaboration & Technical Leadership

• Bring informed technical judgment to architecture discussions — naming the real trade-offs of a pattern (e.g., gateway centralization vs. latency, single- vs multi-agent design) rather than defaulting to what's familiar
• Partner with product, design, and delivery leads across global teams to scope and ship features end-to-end
• Mentor other engineers on the pod and help raise the bar on agentic engineering practices, including AI-assisted development tools like Claude Code
• Own assigned features and releases end-to-end — including the unglamorous parts: debugging, hardening, and keeping production systems trustworthy

What You'll Bring

• 6+ years of professional software engineering experience, including meaningful time shipping GenAI/LLM-powered systems in production — not just prototypes
• Real, hands-on depth in agentic AI: reasoning loops, tool/function calling, multi-agent orchestration, and a considered point of view on when single-agent design beats multi-agent, and why
• Practical RAG expertise: chunking strategies, embeddings, vector databases (OpenSearch or similar), cosine similarity search, and re-ranking — you can explain the mechanism, not just the terminology
• Experience building evaluation and observability for LLM systems: golden datasets, LLM-as-judge, RAGAS or comparable metrics, and tracing tools like LangFuse/LangSmith
• Strong prompt engineering skills — you can write a production system prompt with real structure and constraints on request, not just describe the concept
• Hands-on experience with the AWS GenAI stack: Bedrock, Agent Core, Lambda, DynamoDB (single-table design), S3, SQS, EventBridge, Step Functions
• Strong Python and Node.js skills, with experience building full-stack applications and RESTful APIs
• Solid understanding of production reliability patterns for LLM-backed systems: retries/backoff, circuit breakers, fallback models — as a bundle, not a single tactic
• Experience with containerization (Docker) and cloud-native deployment
• Comfort owning ambiguous, integration-heavy problems, and the ability to go a level deeper when someone pushes on your answer instead of staying abstract

Helpful Extras and Unique Skills

• Direct experience with Amazon Bedrock Agent Core — agent registration, MCP tool wiring, memory/session management, gateway provisioning
• Experience in a regulated or high-stakes domain (education, healthcare, financial services) where evaluation rigor and reliability carry real consequences
• Familiarity with workflow orchestration tools such as Step Functions, Sequencer, or similar state-machine-based systems
• A point of view on LLM observability tooling, gained from actually running it in production rather than reading about it

Our salary range is $176,612 - $243,680 CAD