Développeur principal en IA chez EDC, société canadienne aidant les entreprises à réussir à l’étranger. Conception de systèmes agentiques et intégration de l’IA au cycle de développement logiciel.
About the role
- Staff AI Engineer building production agentic AI and GenAI systems at Robots and Pencils. Designing RAG pipelines, AWS architectures, evaluations, and reliable full-stack integrations.
Responsibilities
- Design and build agentic workflows, including reasoning loops, tool/function calling, and single- and multi-agent architectures
- Build and maintain RAG pipelines with chunking, embeddings, OpenSearch vector search, re-ranking, and 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 LangFuse/LangSmith or equivalent tracing
- Design failure-handling patterns such as retries, circuit breakers, fallback models, and user-facing degradation handling
- 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
- Participate in architecture discussions and communicate technical trade-offs
- Partner with product, design, and delivery leads across global teams
- 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
- 6+ years of professional software engineering experience, including meaningful time shipping GenAI/LLM-powered systems in production
- Hands-on depth in agentic AI: reasoning loops, tool/function calling, and multi-agent orchestration
- Practical RAG expertise, including chunking strategies, embeddings, vector databases such as OpenSearch, 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 LangFuse/LangSmith tracing
- 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; 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
- Ability to own ambiguous, integration-heavy problems
- Helpful extras: Amazon Bedrock Agent Core, regulated or high-stakes domain experience, workflow orchestration tools, and production LLM observability tooling
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