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About the Role
We are seeking a Senior AI Engineer to design and build production-grade agent and RAG systems that power intelligent, reliable automation across our platform. This role requires hands-on engineering, system-level thinking, and ownership from architecture to production reliability. The ideal candidate thrives in ambiguity, moves quickly from prototype to production, and prioritizes quality, safety, and real-world impact.
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 enterprise-grade 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.
- Design and implement LLM post-training strategies, including domain-specific Supervised Fine-Tuning (SFT), DPO/RLHF-based preference alignment, and parameter-efficient fine-tuning techniques like LoRA.
- Build an Agent self-learning feedback loop to convert production execution traces, user feedback, and evaluation results into high-quality datasets for continuous improvement.
- Analyze and optimize LLM behavior across instruction following, tool calling, structured output generation, contextual understanding, reasoning stability, and hallucination mitigation.
- Build production-grade LLM and Agent evaluation frameworks and automated regression pipelines.
- Establish release quality gates and hallucination-detection mechanisms for AI features.
- Build comprehensive AI system observability capabilities, including distributed tracing, structured logging, metrics, dashboards, and alerting.
- Rapidly diagnose and resolve production AI failures.
- Design and implement highly reliable backend services for production AI and Agent workloads.
- Continuously optimize latency, throughput, token consumption, and infrastructure cost.
- Independently diagnose and resolve complex AI system issues.
- Lead technical design for critical modules and system-level capabilities.
- Drive technical improvements based on production data, evaluation results, and benchmarks.
- Prototype, benchmark, and productionize emerging technologies such as GraphRAG, Knowledge Graphs, MCP, LLM Post-Training, and Agent Self-Learning.
- Continuously evaluate Agent frameworks and supporting infrastructure, providing technical recommendations for platform architecture evolution and product technology strategy.
- Stay current with developments in LLM and Agent technologies and rapidly translate promising technologies into production-ready engineering capabilities.
Requirements
- Bachelor's degree or higher in Computer Science, Artificial Intelligence, Electrical Engineering, or a related technical field. Master's or Ph.D. preferred; 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.
- Must have 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.
- Proven ability to diagnose and resolve production LLM/Agent failures.
- Strong Python and distributed backend engineering skills.
- 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.
- Strong experience with RAG and advanced retrieval systems.
- Familiarity with Agent frameworks such as LangGraph, LangChain, AutoGen, and LlamaIndex.
- Experience designing Agent runtime mechanisms, including Human-in-the-Loop workflows.
- Experience with LLM fine-tuning, including LoRA or other parameter-efficient fine-tuning techniques.
- Experience applying LLM technologies to networking, infrastructure, cybersecurity, observability, or other complex technical domains.
- Fluent in both English and Chinese, with strong cross-regional communication and collaboration skills.
Skills
- Agent architectures
- LLM behavioral characteristics
- Instruction following
- Tool-calling behavior
- Context sensitivity
- Multi-step workflows
- Reasoning
- Tool execution
- State management
- Structured outputs
- Validation
- Error recovery
- Production LLM/Agent failure diagnosis and resolution
- Hallucinations
- Incorrect tool calls
- Retrieval-quality degradation
- Agent loops
- Structured-output failures
- Latency regressions
- Prompt changes
- Model changes
- Python
- Distributed backend engineering
- API development
- Service development
- Asynchronous programming
- Concurrent programming
- Retries
- Timeouts
- Caching
- Rate limiting
- Testing
- Logging
- Cross-service performance debugging
- LLM application evaluation systems
- Dataset construction
- Metric definition
- Regression testing
- Release quality gates
- Security risks associated with LLM and Agent applications
- Prompt injection
- Data leakage
- Unsafe tool execution
- Permission boundaries
- Uncontrolled Agent autonomy
- Production system design, implementation, debugging, deployment, and operation
- RAG
- Advanced retrieval systems
- Embeddings
- Vector search
- Hybrid search
- Reranking
- Chunking strategies
- Grounding mechanisms
- Citation mechanisms
- Multi-hop retrieval
- Knowledge Graphs
- GraphRAG
- LangGraph
- LangChain
- AutoGen
- LlamaIndex
- MCP (Model Context Protocol)
- Agent runtime mechanisms
- Human-in-the-Loop workflows
- Risk classification
- Approval workflows
- Interruption workflows
- Pause/resume workflows
- Context management for long-running Agent workflows
- State persistence
- History compression
- Memory systems
- LangSmith
- LLM fine-tuning
- LoRA
- Parameter-efficient fine-tuning
- LLM technologies applied to networking
- LLM technologies applied to infrastructure
- LLM technologies applied to cybersecurity
- LLM technologies applied to observability
Location
- Remote
Work Type
- Full-time
Experience Level
- Senior
- 3+ years of experience in software engineering, machine learning, or applied AI
- 2+ years building, deploying, and operating production-grade LLM or Agent applications
Education Level
- Bachelor's degree or higher in Computer Science, Artificial Intelligence, Electrical Engineering, or a related technical field
- Master's or Ph.D. preferred
Salary/Compensations
- CAD $130,000 - CAD $165,000 + Bonus
Benefits
- RRSP
- Medical coverage
- Dental coverage
About the Company
- Founded in 2004, NetBrain is the leader in no-code network automation.
- Its ground-breaking Next-Gen platform provides IT operations teams with the ability to scale their hybrid multi-cloud connected networks by automating the processes associated with Diagnostic Troubleshooting, Outage Prevention and Protected Change Management.
- Today, over 2,500 of the world’s largest enterprises and managed services providers leverage NetBrain’s platform.
Equal Opportunity
- NetBrain invites all interested and qualified candidates to apply for employment opportunities.
- Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected veteran status, or other characteristics protected by law.
- If you have a disability that prevents or limits your ability to use or access the site, or if you require any other accommodation in the application process due to a disability, you may request a reasonable accommodation.
- In compliance with applicable laws, NetBrain conducts holistic, individual background reviews in support of all hiring decisions.
- It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.