AI Engineer building production AI, Generative AI, and predictive pricing solutions for RBC Capital Markets. Developing scalable desk - facing tools for Sales & Trading.
About the role
- Senior AI Engineer building production Agent and RAG systems for NetBrain’s no-code network automation platform. Improving LLM reliability, observability, and automation at enterprise scale.
Responsibilities
- Design and implement core capabilities for an enterprise-grade Agent platform, including orchestration patterns, tool execution, context and memory management, and safety guardrails
- Design Agent execution and governance mechanisms, including Human-in-the-Loop approval workflows, multi-tenant permission isolation, policy enforcement, and secure execution controls
- Build reusable Agent Skills, standardized tool interfaces, and a scalable tool ecosystem integrated with NetBrain platform capabilities and business workflows
- Design and implement LLM post-training strategies, preference alignment, and parameter-efficient fine-tuning techniques
- Build self-learning feedback loops using production traces, user feedback, and evaluation results
- Analyze and optimize LLM behavior, including instruction following, tool calling, structured outputs, contextual understanding, reasoning stability, and hallucination mitigation
- Build LLM and Agent evaluation frameworks, automated regression pipelines, quality gates, and hallucination-detection mechanisms
- Build AI system observability capabilities including distributed tracing, structured logging, metrics, dashboards, and alerting
- Diagnose and resolve production AI failures and unexpected model behavior changes
- Design reliable backend services for AI and Agent workloads with asynchronous processing, retries, timeouts, caching, rate limiting, and fault isolation
- Optimize latency, throughput, token consumption, and infrastructure cost for large-scale production workloads
- Lead technical design for critical modules and system-level capabilities
- Collaborate with Engineering, Product, QA, and other teams to deliver production solutions
- Prototype, benchmark, and productionize GraphRAG, Knowledge Graphs, MCP, LLM post-training, and Agent self-learning technologies
- Evaluate Agent frameworks and infrastructure and provide recommendations for platform architecture and product technology strategy
Requirements
- Bachelor's degree or higher in Computer Science, Artificial Intelligence, Electrical Engineering, or a related technical field; equivalent practical experience will also be considered
- 3+ years of experience in software engineering, machine learning, or applied AI
- 2+ years building, deploying, and operating production-grade LLM or Agent applications
- Delivered at least one LLM-powered feature end-to-end and owned its ongoing operation and improvement after production launch
- Deep understanding of Agent architectures and LLM behavioral characteristics
- Hands-on experience building multi-step workflows involving reasoning, tool execution, state management, structured outputs, validation, and error recovery
- Ability to diagnose and resolve production LLM/Agent failures
- Strong Python and distributed backend engineering skills
- Experience with API and service development, asynchronous and concurrent programming, retries, timeouts, caching, rate limiting, testing, logging, and cross-service performance debugging
- Hands-on experience designing evaluation systems for LLM applications
- Strong understanding of security risks associated with LLM and Agent applications
- Ability to independently design, implement, debug, deploy, and operate complex production systems
- Preferred: experience with RAG, advanced retrieval systems, Knowledge Graphs, GraphRAG, LangGraph, LangChain, AutoGen, LlamaIndex, MCP, LangSmith, LLM fine-tuning, and applying LLM technologies to complex technical domains
- Fluent in both English and Chinese
Benefits
- Bonus
- RRSP
- Medical/dental coverage
- Comprehensive benefits package
- Reasonable accommodation in the application process
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