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AI Native Engineer
- Vancouver, BC, Canada
Contract
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- Job type
- Contract
- Closing date
- Posting language
- English
- Posted
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Archived listing. The details below describe a past opening.
AI Native Engineer
Location: Vancouver, BC Canada (Hybrid) - Local candidates only
Experience: 7+ Years | Type: Contract/Permanent
Role Summary
We're looking for a hands-on Senior AI Native Engineer to design, build, and ship production-grade AI agents, LLM-integrated applications, and developer productivity tooling on AWS. This is a coding role - the ideal candidate uses GitHub Copilot and/or Claude Code CLI daily and has taken agentic AI or LLM-based solutions beyond POC into real production environments, backed by solid cloud-native engineering, microservices, APIs, and DevOps fundamentals.
Key Responsibilities
- Design, develop, and deploy production-grade AI agents and agentic workflows
- Build LLM-powered applications and integrate AI into enterprise systems
- Develop cloud-native services on AWS; build and maintain microservices, REST APIs, and event-driven integrations
- Use GitHub Copilot and/or Claude Code CLI as part of daily engineering work
- Implement agent workflows using LangGraph, LangChain, CrewAI, AutoGen, Strands, or Bedrock Agents
- Build MCP (Model Context Protocol) integrations and develop MCP servers where applicable
- Design and implement production-grade RAG solutions, including retrieval and evaluation
- Integrate with AWS Bedrock, OpenAI, Anthropic, or other LLM APIs
- Implement LLM observability, tracing, evaluation, and monitoring
- Build AI guardrails, audit logging, and prompt-injection defenses
- Own CI/CD pipelines and support deployment, monitoring, and continuous improvement
- Participate in architecture discussions and code reviews as a strong individual contributor
Must-Have Qualifications
- 7+ years hands-on software engineering (microservices, APIs, cloud-native, enterprise systems)
- 3+ years hands-on AWS and DevOps/CI-CD experience
- Proven experience personally building and deploying an AI agent, LLM integration, or agentic workflow to production
- Active, current IC coding experience (within the last 6 months)
- Demonstrated daily use of GitHub Copilot and/or Claude Code CLI, with real-world examples
Strongly Preferred
- Hands-on MCP server development or MCP-enabled enterprise integration
- Production experience with agent frameworks (LangGraph, LangChain, CrewAI, AutoGen, Strands, Bedrock Agents)
- Strong AWS Bedrock production experience
- Internal developer platform / engineering productivity tooling experience
- Production RAG with measurable retrieval quality and eval frameworks
- LLM observability/eval experience (Langfuse, LangSmith, Braintrust, W&B)
- AI governance experience - guardrails, prompt-injection protection, access controls, audit logging
- Secure enterprise integration design between AI agents and internal systems
More about this job
Responsibilities
Design, develop, and deploy production-grade AI agents and agentic workflows using LLMs. Build and maintain cloud-native services, microservices, and event-driven integrations on AWS.
Requirements
Requires 7+ years of software engineering experience with a strong focus on microservices, APIs, and cloud-native systems. Candidates must have 3+ years of hands-on AWS experience and proven success in deploying AI agents or LLM integrations into production.
Skills
- AI Agents
- LLM
- AWS
- Microservices
- REST APIs
- GitHub Copilot
- Claude Code CLI
- LangGraph
- LangChain
- CrewAI
- AutoGen
- RAG
- AWS Bedrock
- CI/CD
- DevOps
- Model Context Protocol
Visa sponsorship
Not detected in the job text
Categories
- Software
- Technology
- Engineering
- Data & Analytics
- Consulting
Keywords
- AI Native Engineer
- LLM
- AI Agents
- AWS
- Microservices
- REST APIs
- GitHub Copilot
- Claude Code CLI
- LangGraph
- LangChain
- CrewAI
- AutoGen
- Bedrock Agents
- Model Context Protocol
- RAG
- AWS Bedrock
- CI/CD
- DevOps
- Observability
- Tracing
- Prompt-injection
- Cloud-native
- Enterprise Systems
- Software Engineering
- Agentic Workflows
- Guardrails
- Audit Logging
- Event-driven
- Engineering Productivity
- MCP